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  • NATURE INDEX
  • 12 October 2022

Growth in AI and robotics research accelerates

It may not be unusual for burgeoning areas of science, especially those related to rapid technological changes in society, to take off quickly, but even by these standards the rise of artificial intelligence (AI) has been impressive. Together with robotics, AI is representing an increasingly significant portion of research volume at various levels, as these charts show.

Across the field

The number of AI and robotics papers published in the 82 high-quality science journals in the Nature Index (Count) has been rising year-on-year — so rapidly that it resembles an exponential growth curve. A similar increase is also happening more generally in journals and proceedings not included in the Nature Index, as is shown by data from the Dimensions database of research publications.

Bar charts comparing AI and robotics publications in Nature Index and Dimensions

Source: Nature Index, Dimensions. Data analysis by Catherine Cheung; infographic by Simon Baker, Tanner Maxwell and Benjamin Plackett

Leading countries

Five countries — the United States, China, the United Kingdom, Germany and France — had the highest AI and robotics Share in the Nature Index from 2015 to 2021, with the United States leading the pack. China has seen the largest percentage change (1,174%) in annual Share over the period among the five nations.

Line graph showing the rise in Share for the top 5 countries in AI and robotics

AI and robotics infiltration

As the field of AI and robotics research grows in its own right, leading institutions such as Harvard University in the United States have increased their Share in this area since 2015. But such leading institutions have also seen an expansion in the proportion of their overall index Share represented by research in AI and robotics. One possible explanation for this is that AI and robotics is expanding into other fields, creating interdisciplinary AI and robotics research.

Graphs showing Share of the 5 leading institutions in AI and robotics

Nature 610 , S9 (2022)

doi: https://doi.org/10.1038/d41586-022-03210-9

This article is part of Nature Index 2022 AI and robotics , an editorially independent supplement. Advertisers have no influence over the content.

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IEEE Transactions on Robotics (T-RO)

-  ICRA@40 :    September 1, 2023 to June 30, 2024

The IEEE Transactions on Robotics (T-RO)  publishes research papers that represent major advances in the state-of-the-art in all areas of robotics. The Transactions welcomes original papers that report on any combination of theory, design, experimental studies, analysis, algorithms, and integration and application case studies involving all aspects of robotics. You can learn more about T-RO's scope, paper length policy, open access option, and preparation of papers for submission at the  Information for Authors page .

As of late May 2020, T-RO no longer has a "short paper" category for new submissions.  Papers that are short may still be published, but they are treated as Regular paper submissions, and they are subject to the same standards for significance.  Authors of short papers (8 pages or fewer) may consider our sister journal, the  IEEE Robotics and Automation Letters  (RA-L).

Table of Contents of the latest T-RO issue ( IEEE Xplore ) Early Access Articles Most Downloaded Articles Special Collections

Joining the Transactions on Robotics Editorial Board

Presenting your transactions on robotics paper at icra, iros, and case.

Any IEEE Transactions on Robotics (T-RO) paper, other than communication items and survey papers, may be presented at either an upcoming IEEE International Conference on Robotics and Automation (ICRA), an upcoming IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), or International Conference On Automation Science and Engineering (CASE), provided most of the key ideas of the paper have never appeared at a conference with a published proceedings (i.e., the paper is a "new" paper and not the evolved version of a previous conference paper or papers). For conference eligibility deadlines, see the RAS conference dates in the blue box above.

Authors may not request any acceleration or delay of the review process based on these criteria.

Upon final notification of acceptance, eligible papers will be offered an option to present at conference in the author's workspace within the PaperCept platform. The prompt within the workspace will include an option to transfer the paper directly to conference organizers. Authors will have a window of one month to select and accept which conference they will present at. Authors are expected to pay the conference fee. Eligible papers may only be presented at one conference.

Historically papers in the Transactions on Robotics have been either "evolutionary" papers (papers extended, with new results, from previously presented conference papers by the same authors) or "new" direct-to-journal papers (papers that are not evolved from conference papers).  Since the introduction of the Robotics and Automation Letters (RA-L), the robotics community has demonstrated strong support for direct-to-journal papers (maximum of eight pages) with the possibility of presentation at a conference.

This IEEE RAS policy, adopted by AdCom in September 2017 and formalizing pilots of the policy at ICRA 2017 and 2018, provides a conference presentation option for "new" direct-to-journal T-RO papers.  Authors are no longer forced to write two versions of the paper (a short one for conference presentation and a longer one for the "final" journal version) if they want the work both to be presented at a conference and to appear in a journal.  This saves on author and reviewer effort, eliminates the confusion over which paper to cite, and reduces the stress on authors and reviewers arising due to submission deadlines for ICRA, IROS, or CASE. The new policy gives a new benefit to T-RO authors and brings high-quality T-RO papers to ICRA, IROS, or CASE without harming the traditional evolutionary model.

Is My Paper "Evolved" or "New?"

This initiative distinguishes between papers that have evolved directly from conference papers ("evolved" papers) and papers that have not ("new" direct-to-journal papers).  Of course the distinction is not always clear-cut, since almost all of one's research has evolved in some way from one's previous papers.

Below are some criteria to consider in the judgment of whether a paper is evolved or new.  If the answer to one or more of these questions is "yes," this is a good sign that your paper should be considered to be evolved.

  • Does the journal paper have the same title as the previous conference paper?
  • Is there a direct lineage from the conference paper(s) to the journal paper?
  • Typically a paper has one or a small number of key new ideas.  (There may be many supporting details.)  Does a majority of the key ideas in the T-RO paper appear in the previous conference paper(s)?
  • Would the T-RO paper have been rejected without the content of the previous conference paper(s)?
  • Does the T-RO paper use a significant amount of text, results, data, or figures from the previous conference paper(s)?

An advantage of having your paper be considered "evolved" is that you are free to incorporate much of the material from your conference paper(s) without penalty in the review process, provided the new paper provides a significant contribution beyond the conference paper(s) (see the guidance here for more details).  The disadvantage is that your "evolved" paper is not eligible for presentation at ICRA, IROS, or CASE.  The disadvantage of declaring your paper "new" is that you cannot reuse significant portions of the material from your conference paper(s), but the advantage is that the new paper (if accepted) is eligible for presentation at ICRA, IROS, or CASE.

Note that no submission can be considered to be "evolved" from a paper that previously appeared in a journal (including the IEEE Robotics and Automation Letters).

If you are in doubt, send your brief analysis along with the T-RO paper and the relevant conference paper(s) to the Editor-in-Chief for an evaluation.  It is unethical to withhold relevant previous conference paper(s) in this analysis.

IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award

2023:  " RACER: Rapid Collaborative Exploration with a Decentralized Multi-UAV System "   by Boyu Zhou, Hao Xu, and Shaojie Shen   vol. 39, no. 3, pp. 1816-1835, June 2023, [ Xplore Link ]

Honorable Mention

"Global Planning for Contact-Rich Manipulation via Local Smoothing of Quasi-dynamic Contact Models" [ Xplore Link ]

"Grasp it Like a Pro 2.0: A Data-Driven Approach Exploiting Basic Shapes Decomposition and Human Data for Grasping Unknown Objects" [ Xplore Link ]

"Kinegami: Algorithmic Design of Compliant Kinematic Chains from Tubular Origami" [ Xplore Link ]

"ANYexo 2.0: A Fully-Actuated Upper-Limb Exoskeleton for Manipulation and Joint-Oriented Training in all Stages of Rehabilitation" [ Xplore Link ]

"Perceptive Locomotion through Nonlinear Model Predictive Control" [ Xplore Link ]

2022:  " Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems "   by Yulun Tian; Yun Chang; Fernando Herrera Arias; Carlos Nieto-Granda; Jonathan P. How; Luca Carlone   vol. 38, no. 4, pp. 2022-2038, August 2022, [ Xplore Link ]

"Stabilization of Complementarity Systems via Contact-Aware Controllers"   [ Xplore Link ]

"Autonomous Cave Surveying With an Aerial Robot"   [ Xplore Link ]

"Prehensile Manipulation Planning: Modeling, Algorithms and Implementation"   [ Xplore Link ]

"Rock-and-Walk Manipulation: Object Locomotion by Passive Rolling Dynamics and Periodic Active Control"   [ Xplore Link ]

        "Origami-Inspired Soft Actuators for Stimulus Perception and Crawling Robot Applications"   [ Xplore Link ]

2021:  " Collision Resilient Insect-scale Soft-actuated Aerial Robots With High Agility "   by YuFeng Chen; Siyi Xu; Zhijian Ren; Pakpong Chirarattananon   vol. 37, no. 5, pp. 1752-1764, October 2021, [ Xplore Link ]

"A Backdrivable Kinematically Redundant (6+3)-dof Hybrid Parallel Robot for Intuitive Sensorless Physical Human-Robot Interaction"   [ Xplore Link ]

"Stochastic Dynamic Games in Belief Space"   [ Xplore Link ]

"ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM"   [ Xplore Link ]

"Active Interaction Force Control for Contact-Based Inspection with a Fully Actuated Aerial Vehicle"   [ Xplore Link ]

        "Distributed Certifiably Correct Pose-Graph Optimization"   [ Xplore Link ]

2020: "TossingBot: Learning to Throw Arbitrary Objects With Residual Physics"   by Andy Zeng; Shuran Song; Johnny Lee; Alberto Rodriguez; Thomas Funkhouser vol. 36, no. 4, pp. 1307-1319, August 2020, [ Xplore Link ]

"Design and Validation of a Powered Knee-Ankle Prosthesis With High-Torque, Low-Impedance Actuators" [ Xplore Link ]

"Quantifying Hypothesis Space Misspecification in Learning From Human-Robot Demonstrations and Physical Corrections" [ Xplore Link ]

"Teach-Repeat-Replan: A Complete and Robust System for Aggressive Flight in Complex Environments"    [ Xplore Link ]

"Deep Drone Racing: From Simulation to Reality With Domain Randomization"    [ Xplore Link ]

2019: "Active Learning of Dynamics for Data-Driven Control Using Koopman Operators"   by Ian Abraham and Todd D. Murphey   vol. 35, no. 5, pp. 1071-1083, October 2019, [ Xplore Link ]

2018: "Grasping Without Squeezing: Design and Modeling of Shear-Activated Grippers"   by Elliot Wright Hawkes, Hao Jiang, David L. Christensen, Amy K. Han, and Mark R. Cutkosky   vol. 34, no. 2, pp. 303-316, April 2018, [ Xplore Link ]

"Exploiting Elastic Energy Storage for “Blind” Cyclic Manipulation: Modeling, Stability Analysis, Control, and Experiments for Dribbling"   [ Xplore Link ]

"VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator"  [ Xplore Link ]

2017: "On-Manifold Preintegration for Real-Time Visual-Inertial Odometry"   by Christian Forster, Luca Carlone, Frank Dellaert, and Davide Scaramuzza   vol. 33, no. 1, pp. 1-21, February 2017, [ Xplore Link ]

2016: "Rapidly Exploring Random Cycles: Persistent Estimation of Spatiotemporal Fields With Multiple Sensing Robots"   by Xiaodong Lan and Mac Schwager   vol. 32, no. 5, pp. 1230-1244, October 2016, [ Xplore Link ]

2015:  " ORB-SLAM: A Versatile and Accurate Monocular SLAM System" by  Raul Mur-Artal, J. M. M. Montiel and Juan D. Tardos vol. 31, no. 5, pp. 1147-1163, 2015 [ Xplore Link ].

2014:  " Catching Objects in Flight" by  Seungsu Kim, Ashwini Shukla, Aude Billard vol. 30, no. 5, pp. 1049-1065, 2014 [ Xplore Link ].

2013: " Robots Driven by Compliant Actuators: Optimal Control under Actuation Constraints" by  David J. Braun, Florian Petit, Felix Huber, Sami Haddadin, Patrick van der Smagt, Alin Albu-Schäffer, Sethu Vijayakumar vol. 29, no. 5, pp. 1085-1101, 2013 [ Xplore Link ].

2012: " Reinforcement Learning With Sequences of Motion Primitives for Robust Manipulation" by  Freek Stulp, Evangelos A. Theodorou, Stefan Schaal vol. 28, no. 6, pp. 1360-1370, 2012 [ Xplore Link ].

2011: " Human-Like Adaptation of Force and Impedance in Stable and Unstable Interactions" by  Chenguang Yang, Gowrishankar Ganesh, Sami Haddadin, Sven Parusel, Alin Albu-Schaeffer, Etienne Burdet vol. 27, no. 5, pp. 918-930, 2011 [ Xplore Link ].

2010: " Design and Control of Concentric-Tube Robots" by  Pierre E. Dupont, Jesse Lock, Brandon Itkowitz, Evan Butler vol. 26, no. 2, pp. 209-225, 2010 [ Xplore Link ].

2009: " Vision-Aided Inertial Navigation for Spacecraft Entry, Descent, and Landing" by  Anastasios I. Mourikis, Nikolas Trawny, Stergios I. Roumeliotis, Andrew E. Johnson, Adnan Ansar, Larry Matthies vol. 25, no, 2, pp. 264-280, 2009 [ Xplore Link ].

2008: " Smooth Vertical Surface Climbing with Directional Adhesion" by  Sangbae Kim, Matthew Spenko, Salomon Trujillo, Barrett Heyneman, Daniel Santos, Mark R. Cutkosky vol. 24, no. 1, pp. 65-74, 2008 [ Xplore Link ].

2007: " Manipulation Planning for Deformable Linear Objects" by  Mitul Saha, Pekka Isto vol. 23, no. 6, pp. 1141-1150, 2007 [ Xplore Link ].

2006: " Exactly Sparse Delayed-State Filters for View-Based SLAM" by  Ryan M. Eustice, Hanumant Singh, John J. Leonard vol. 22, no. 6, pp. 1100-1114, 2006 [ Xplore Link ].

2005: " Active Filtering of Physiological Motion in Robotized Surgery Using Predictive Control" by  Romuald Ginhoux, Jacques Gangloff, Michel de Mathelin,Luc Soler, Mara M. Arenas Sanchez, Jacques Marescaux vol. 21, no. 1, pp. 67-79, 2005 [ Xplore Link ].

2004: " Reactive Path Deformation for Nonholonomic Mobile Robots" by  Florent Lamiraux, David Bonnafous, Olivier Lefebvre vol. 20, no. 6, pp. 967-977, 2004 [ Xplore Link ].

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The use of robotics in shipwreck discovery

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500 research papers and projects in robotics – Free Download

research paper in robot

The recent history of robotics is full of fascinating moments that accelerated the rapid technological advances in artificial intelligence , automation , engineering, energy storage, and machine learning. The result transformed the capabilities of robots and their ability to take over tasks once carried out by humans at factories, hospitals, farms, etc.

These technological advances don’t occur overnight; they require several years of research and development in solving some of the biggest engineering challenges in navigation, autonomy, AI and machine learning to build robots that are much safer and efficient in a real-world situation. A lot of universities, institutes, and companies across the world are working tirelessly in various research areas to make this reality.

In this post, we have listed 500+ recent research papers and projects for those who are interested in robotics. These free, downloadable research papers can shed lights into the some of the complex areas in robotics such as navigation, motion planning, robotic interactions, obstacle avoidance, actuators, machine learning, computer vision, artificial intelligence, collaborative robotics, nano robotics, social robotics, cloud, swan robotics, sensors, mobile robotics, humanoid, service robots, automation, autonomous, etc. Feel free to download. Share your own research papers with us to be added into this list. Also, you can ask a professional academic writer from  CustomWritings – research paper writing service  to assist you online on any related topic.

Navigation and Motion Planning

  • Robotics Navigation Using MPEG CDVS
  • Design, Manufacturing and Test of a High-Precision MEMS Inclination Sensor for Navigation Systems in Robot-assisted Surgery
  • Motion Control of a Three Active Wheeled Mobile Robot and Collision-Free Human Following Navigation in Outdoor Environment
  • One Point Perspective Vanishing Point Estimation for Mobile Robot Vision Based Navigation System
  • Application of Ant Colony Optimization for finding the Navigational path of Mobile Robot-A Review
  • Robot Navigation Using a Brain-Computer Interface
  • Path Generation for Robot Navigation using a Single Ceiling Mounted Camera
  • Exact Robot Navigation Using Power Diagrams
  • Learning Socially Normative Robot Navigation Behaviors with Bayesian Inverse Reinforcement Learning
  • Pipelined, High Speed, Low Power Neural Network Controller for Autonomous Mobile Robot Navigation Using FPGA
  • Proxemics models for human-aware navigation in robotics: Grounding interaction and personal space models in experimental data from psychology
  • Optimality and limit behavior of the ML estimator for Multi-Robot Localization via GPS and Relative Measurements
  • Aerial Robotics: Compact groups of cooperating micro aerial vehicles in clustered GPS denied environment
  • Disordered and Multiple Destinations Path Planning Methods for Mobile Robot in Dynamic Environment
  • Integrating Modeling and Knowledge Representation for Combined Task, Resource and Path Planning in Robotics
  • Path Planning With Kinematic Constraints For Robot Groups
  • Robot motion planning for pouring liquids
  • Implan: Scalable Incremental Motion Planning for Multi-Robot Systems
  • Equilibrium Motion Planning of Humanoid Climbing Robot under Constraints
  • POMDP-lite for Robust Robot Planning under Uncertainty
  • The RoboCup Logistics League as a Benchmark for Planning in Robotics
  • Planning-aware communication for decentralised multi- robot coordination
  • Combined Force and Position Controller Based on Inverse Dynamics: Application to Cooperative Robotics
  • A Four Degree of Freedom Robot for Positioning Ultrasound Imaging Catheters
  • The Role of Robotics in Ovarian Transposition
  • An Implementation on 3D Positioning Aquatic Robot

Robotic Interactions

  • On Indexicality, Direction of Arrival of Sound Sources and Human-Robot Interaction
  • OpenWoZ: A Runtime-Configurable Wizard-of-Oz Framework for Human-Robot Interaction
  • Privacy in Human-Robot Interaction: Survey and Future Work
  • An Analysis Of Teacher-Student Interaction Patterns In A Robotics Course For Kindergarten Children: A Pilot Study
  • Human Robotics Interaction (HRI) based Analysis–using DMT
  • A Cautionary Note on Personality (Extroversion) Assessments in Child-Robot Interaction Studies
  • Interaction as a bridge between cognition and robotics
  • State Representation Learning in Robotics: Using Prior Knowledge about Physical Interaction
  • Eliciting Conversation in Robot Vehicle Interactions
  • A Comparison of Avatar, Video, and Robot-Mediated Interaction on Users’ Trust in Expertise
  • Exercising with Baxter: Design and Evaluation of Assistive Social-Physical Human- Robot Interaction
  • Using Narrative to Enable Longitudinal Human- Robot Interactions
  • Computational Analysis of Affect, Personality, and Engagement in HumanRobot Interactions
  • Human-robot interactions: A psychological perspective
  • Gait of Quadruped Robot and Interaction Based on Gesture Recognition
  • Graphically representing child- robot interaction proxemics
  • Interactive Demo of the SOPHIA Project: Combining Soft Robotics and Brain-Machine Interfaces for Stroke Rehabilitation
  • Interactive Robotics Workshop
  • Activating Robotics Manipulator using Eye Movements
  • Wireless Controlled Robot Movement System Desgined using Microcontroller
  • Gesture Controlled Robot using LabVIEW
  • RoGuE: Robot Gesture Engine

Obstacle Avoidance

  • Low Cost Obstacle Avoidance Robot with Logic Gates and Gate Delay Calculations
  • Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance
  • Controlling Obstacle Avoiding And Live Streaming Robot Using Chronos Watch
  • Movement Of The Space Robot Manipulator In Environment With Obstacles
  • Assis-Cicerone Robot With Visual Obstacle Avoidance Using a Stack of Odometric Data.
  • Obstacle detection and avoidance methods for autonomous mobile robot
  • Moving Domestic Robotics Control Method Based on Creating and Sharing Maps with Shortest Path Findings and Obstacle Avoidance
  • Control of the Differentially-driven Mobile Robot in the Environment with a Non-Convex Star-Shape Obstacle: Simulation and Experiments
  • A survey of typical machine learning based motion planning algorithms for robotics
  • Linear Algebra for Computer Vision, Robotics , and Machine Learning
  • Applying Radical Constructivism to Machine Learning: A Pilot Study in Assistive Robotics
  • Machine Learning for Robotics and Computer Vision: Sampling methods and Variational Inference
  • Rule-Based Supervisor and Checker of Deep Learning Perception Modules in Cognitive Robotics
  • The Limits and Potentials of Deep Learning for Robotics
  • Autonomous Robotics and Deep Learning
  • A Unified Knowledge Representation System for Robot Learning and Dialogue

Computer Vision

  • Computer Vision Based Chess Playing Capabilities for the Baxter Humanoid Robot
  • Non-Euclidean manifolds in robotics and computer vision: why should we care?
  • Topology of singular surfaces, applications to visualization and robotics
  • On the Impact of Learning Hierarchical Representations for Visual Recognition in Robotics
  • Focused Online Visual-Motor Coordination for a Dual-Arm Robot Manipulator
  • Towards Practical Visual Servoing in Robotics
  • Visual Pattern Recognition In Robotics
  • Automated Visual Inspection: Position Identification of Object for Industrial Robot Application based on Color and Shape
  • Automated Creation of Augmented Reality Visualizations for Autonomous Robot Systems
  • Implementation of Efficient Night Vision Robot on Arduino and FPGA Board
  • On the Relationship between Robotics and Artificial Intelligence
  • Artificial Spatial Cognition for Robotics and Mobile Systems: Brief Survey and Current Open Challenges
  • Artificial Intelligence, Robotics and Its Impact on Society
  • The Effects of Artificial Intelligence and Robotics on Business and Employment: Evidence from a survey on Japanese firms
  • Artificially Intelligent Maze Solver Robot
  • Artificial intelligence, Cognitive Robotics and Human Psychology
  • Minecraft as an Experimental World for AI in Robotics
  • Impact of Robotics, RPA and AI on the insurance industry: challenges and opportunities

Probabilistic Programming

  • On the use of probabilistic relational affordance models for sequential manipulation tasks inrobotics
  • Exploration strategies in developmental robotics: a unified probabilistic framework
  • Probabilistic Programming for Robotics
  • New design of a soft-robotics wearable elbow exoskeleton based on Shape Memory Alloy wires actuators
  • Design of a Modular Series Elastic Upgrade to a Robotics Actuator
  • Applications of Compliant Actuators to Wearing Robotics for Lower Extremity
  • Review of Development Stages in the Conceptual Design of an Electro-Hydrostatic Actuator for Robotics
  • Fluid electrodes for submersible robotics based on dielectric elastomer actuators
  • Cascaded Control Of Compliant Actuators In Friendly Robotics

Collaborative Robotics

  • Interpretable Models for Fast Activity Recognition and Anomaly Explanation During Collaborative Robotics Tasks
  • Collaborative Work Management Using SWARM Robotics
  • Collaborative Robotics : Assessment of Safety Functions and Feedback from Workers, Users and Integrators in Quebec
  • Accessibility, Making and Tactile Robotics : Facilitating Collaborative Learning and Computational Thinking for Learners with Visual Impairments
  • Trajectory Adaptation of Robot Arms for Head-pose Dependent Assistive Tasks

Mobile Robotics

  • Experimental research of proximity sensors for application in mobile robotics in greenhouse environment.
  • Multispectral Texture Mapping for Telepresence and Autonomous Mobile Robotics
  • A Smart Mobile Robot to Detect Abnormalities in Hazardous Zones
  • Simulation of nonlinear filter based localization for indoor mobile robot
  • Integrating control science in a practical mobile robotics course
  • Experimental Study of the Performance of the Kinect Range Camera for Mobile Robotics
  • Planification of an Optimal Path for a Mobile Robot Using Neural Networks
  • Security of Networking Control System in Mobile Robotics (NCSMR)
  • Vector Maps in Mobile Robotics
  • An Embedded System for a Bluetooth Controlled Mobile Robot Based on the ATmega8535 Microcontroller
  • Experiments of NDT-Based Localization for a Mobile Robot Moving Near Buildings
  • Hardware and Software Co-design for the EKF Applied to the Mobile Robotics Localization Problem
  • Design of a SESLogo Program for Mobile Robot Control
  • An Improved Ekf-Slam Algorithm For Mobile Robot
  • Intelligent Vehicles at the Mobile Robotics Laboratory, University of Sao Paolo, Brazil [ITS Research Lab]
  • Introduction to Mobile Robotics
  • Miniature Piezoelectric Mobile Robot driven by Standing Wave
  • Mobile Robot Floor Classification using Motor Current and Accelerometer Measurements
  • Sensors for Robotics 2015
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  • Design Of Control System For Articulated Robot Using Leap Motion Sensor
  • Automated configuration of vision sensor systems for industrial robotics

Nano robotics

  • Light Robotics: an all-optical nano-and micro-toolbox
  • Light-driven Nano- robotics
  • Light-driven Nano-robotics
  • Light Robotics: a new tech–nology and its applications
  • Light Robotics: Aiming towards all-optical nano-robotics
  • NanoBiophotonics Appli–cations of Light Robotics
  • System Level Analysis for a Locomotive Inspection Robot with Integrated Microsystems
  • High-Dimensional Robotics at the Nanoscale Kino-Geometric Modeling of Proteins and Molecular Mechanisms
  • A Study Of Insect Brain Using Robotics And Neural Networks

Social Robotics

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  • ProCRob Architecture for Personalized Social Robotics
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  • Preliminary system and hardware design for Quori, a low-cost, modular, socially interactive robot
  • Socially assistive robotics: Human augmentation versus automation
  • Tega: A Social Robot

Humanoid robot

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  • Literature Review Real-Time Vision-Based Learning for Human-Robot Interaction in Social Humanoid Robotics
  • The Roasted Tomato Challenge for a Humanoid Robot
  • Remotely teleoperating a humanoid robot to perform fine motor tasks with virtual reality

Cloud Robotics

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  • A Software Product Line Approach for Configuring Cloud Robotics Applications
  • Cloud robotics and automation: A survey of related work
  • ROCHAS: Robotics and Cloud-assisted Healthcare System for Empty Nester

Swarm Robotics

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  • A Concise Chronological Reassess Of Different Swarm Intelligence Methods With Multi Robotics Approach
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  • Task Allocation in Foraging Robot Swarms: The Role of Information Sharing
  • Robotics on the Battlefield Part II
  • Implementation Of Load Sharing Using Swarm Robotics
  • An Investigation of Environmental Influence on the Benefits of Adaptation Mechanisms in Evolutionary Swarm Robotics

Soft Robotics

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  • Soft Robotics: Transferring Theory to Application,” Soft Components for Soft Robots”
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  • Soft Brain-Machine Interfaces for Assistive Robotics: A Novel Control Approach
  • Towards A Robot Hardware ABSTRACT ion Layer (R-HAL) Leveraging the XBot Software Framework

Service Robotics

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  • Meshworm With Segment-Bending Anchoring for Colonoscopy. IEEE ROBOTICS AND AUTOMATION LETTERS. 2 (3) pp: 1718-1724.
  • Recent Advances in Robotics and Automation
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  • Assessing the Impact of an Autonomous Robotics Competition for STEM Education
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Construction

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  • Why robotics in mining
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  • Mining Robotics
  • TIRAMISU: Technical survey, close-in-detection and disposal mine actions in Humanitarian Demining: challenges for Robotics Systems
  • Robotics for Sustainable Agriculture in Aquaponics
  • Design and Fabrication of Crop Analysis Agriculture Robot
  • Enhance Multi-Disciplinary Experience for Agriculture and Engineering Students with Agriculture Robotics Project
  • Work in progress: Robotics mapping of landmine and UXO contaminated areas
  • Robot Based Wireless Monitoring and Safety System for Underground Coal Mines using Zigbee Protocol: A Review
  • Minesweepers uses robotics’ awesomeness to raise awareness about landminesexplosive remnants of war
  • Intelligent Autonomous Farming Robot with Plant Disease Detection using Image Processing
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  • OP: Sense, a rapid prototyping research platform for surgical robotics
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Defence and Military

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  • BORDER SECURITY ROBOT
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Space Robotics

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  • Model-based Apprenticeship Learning for Robotics in High-dimensional Spaces
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  • Reaction Null Space of a multibody system with applications in robotics

Other Industries

  • Robotics in clothes manufacture
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  • Architecture for theatre robotics
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  • A Robot-based Application for Physical Exercise Training
  • Application Of Robotics In Oil And Gas Refineries
  • Implementation of Robotics in Transmission Line Monitoring
  • Intelligent Wireless Fire Extinguishing Robot
  • Monitoring and Controlling of Fire Fighthing Robot using IOT
  • Robotics An Emerging Technology in Dairy Industry
  • Robotics and Law: A Survey
  • Increasing ECE Student Excitement through an International Marine Robotics Competition
  • Application of Swarm Robotics Systems to Marine Environmental Monitoring

Future of Robotics / Trends

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  • Published: 10 February 2023

Trends and research foci of robotics-based STEM education: a systematic review from diverse angles based on the technology-based learning model

  • Darmawansah Darmawansah   ORCID: orcid.org/0000-0002-3464-4598 1 ,
  • Gwo-Jen Hwang   ORCID: orcid.org/0000-0001-5155-276X 1 , 3 ,
  • Mei-Rong Alice Chen   ORCID: orcid.org/0000-0003-2722-0401 2 &
  • Jia-Cing Liang   ORCID: orcid.org/0000-0002-1134-527X 1  

International Journal of STEM Education volume  10 , Article number:  12 ( 2023 ) Cite this article

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Fostering students’ competence in applying interdisciplinary knowledge to solve problems has been recognized as an important and challenging issue globally. This is why STEM (Science, Technology, Engineering, Mathematics) education has been emphasized at all levels in schools. Meanwhile, the use of robotics has played an important role in STEM learning design. The purpose of this study was to fill a gap in the current review of research on Robotics-based STEM (R-STEM) education by systematically reviewing existing research in this area. This systematic review examined the role of robotics and research trends in STEM education. A total of 39 articles published between 2012 and 2021 were analyzed. The review indicated that R-STEM education studies were mostly conducted in the United States and mainly in K-12 schools. Learner and teacher perceptions were the most popular research focus in these studies which applied robots. LEGO was the most used tool to accomplish the learning objectives. In terms of application, Technology (programming) was the predominant robotics-based STEM discipline in the R-STEM studies. Moreover, project-based learning (PBL) was the most frequently employed learning strategy in robotics-related STEM research. In addition, STEM learning and transferable skills were the most popular educational goals when applying robotics. Based on the findings, several implications and recommendations to researchers and practitioners are proposed.

Introduction

Over the past few years, implementation of STEM (Science, Technology, Engineering, and Mathematics) education has received a positive response from researchers and practitioners alike. According to Chesloff ( 2013 ), the winning point of STEM education is its learning process, which validates that students can use their creativity, collaborative skills, and critical thinking skills. Consequently, STEM education promotes a bridge between learning in authentic real-life scenarios (Erdoğan et al., 2016 ; Kelley & Knowles, 2016 ). This is the greatest challenge facing STEM education. The learning experience and real-life situation might be intangible in some areas due to pre- and in-conditioning such as unfamiliarity with STEM content (Moomaw, 2012 ), unstructured learning activities (Sarama & Clements, 2009), and inadequate preparation of STEM curricula (Conde et al., 2021 ).

In response to these issues, the adoption of robotics in STEM education has been encouraged as part of an innovative and methodological approach to learning (Bargagna et al., 2019 ; Ferreira et al., 2018 ; Kennedy et al., 2015 ; Köse et al., 2015 ). Similarly, recent studies have reported that the use of robots in school settings has an impact on student curiosity (Adams et al., 2011 ), arts and craftwork (Sullivan & Bers, 2016 ), and logic (Bers, 2008 ). When robots and educational robotics are considered a core part of STEM education, it offers the possibility to promote STEM disciplines such as engineering concepts or even interdisciplinary practices (Okita, 2014 ). Anwar et. al. ( 2019 ) argued that integration between robots and STEM learning is important to support STEM learners who do not immediately show interest in STEM disciplines. Learner interest can elicit the development of various skills such as computational thinking, creativity and motivation, collaboration and cooperation, problem-solving, and other higher-order thinking skills (Evripidou et al., 2020 ). To some extent, artificial intelligence (AI) has driven the use of robotics and tools, such as their application to designing instructional activities (Hwang et al., 2020 ). The potential for research on robotics in STEM education can be traced by showing the rapid increase in the number of studies over the past few years. The emphasis is on critically reviewing existing research to determine what prior research already tells us about R-STEM education, what it means, and where it can influence future research. Thus, this study aimed to fill the gap by conducting a systematic review to grasp the potential of R-STEM education.

In terms of providing the core concepts of roles and research trends of R-STEM education, this study explored beyond the scope of previous reviews by conducting content analysis to see the whole picture. To address the following questions, this study analyzed published research in the Web of Science database regarding the technology-based learning model (Lin & Hwang, 2019 ):

In terms of research characteristic and features, what were the location, sample size, duration of intervention, research methods, and research foci of the R-STEM education research?

In terms of interaction between participants and robots, what were the participants, roles of the robot, and types of robot in the R-STEM education research?

In terms of application, what were the dominant STEM disciplines, contribution to STEM disciplines, integration of robots and STEM, pedagogical interventions, and educational objectives of the R-STEM research?

  • Literature review

Previous studies have investigated the role of robotics in R-STEM education from several research foci such as the specific robot users (Atman Uslu et al., 2022 ; Benitti, 2012 ; Jung & Won, 2018 ; Spolaôr & Benitti, 2017 ; van den Berghe et al., 2019 ), the potential value of R-STEM education (Çetin & Demircan, 2020 ; Conde et al., 2021 ; Zhang et al., 2021 ), and the types of robots used in learning practices (Belpaeme et al., 2018 ; Çetin & Demircan, 2020 ; Tselegkaridis & Sapounidis, 2021 ). While their findings provided a dynamic perspective on robotics, they failed to contribute to the core concept of promoting R-STEM education. Those previous reviews did not summarize the exemplary practice of employing robots in STEM education. For instance, Spolaôr and Benitti ( 2017 ) concluded that robots could be an auxiliary tool for learning but did not convey whether the purpose of using robots is essential to enhance learning outcomes. At the same time, it is important to address the use and purpose of robotics in STEM learning, the connections between theoretical pedagogy and STEM practice, and the reasons for the lack of quantitative research in the literature to measure student learning outcomes.

First, Benitti ( 2012 ) reviewed research published between 2000 and 2009. This review study aimed to determine the educational potential of using robots in schools and found that it is feasible to use most robots to support the pedagogical process of learning knowledge and skills related to science and mathematics. Five years later, Spolaôr and Benitti ( 2017 ) investigated the use of robots in higher education by employing the adopted-learning theories that were not covered in their previous review in 2012. The study’s content analysis approach synthesized 15 papers from 2002 to 2015 that used robots to support instruction based on fundamental learning theory. The main finding was that project-based learning (PBL) and experiential learning, or so-called hands-on learning, were considered to be the most used theories. Both theories were found to increase learners’ motivation and foster their skills (Behrens et al., 2010 ; Jou et al., 2010 ). However, the vast majority of discussions of the selected reviews emphasized positive outcomes while overlooking negative or mixed outcomes. Along the same lines, Jung and Won ( 2018 ) also reviewed theoretical approaches to Robotics education in 47 studies from 2006 to 2017. Their focused review of studies suggested that the employment of robots in learning should be shifted from technology to pedagogy. This review paper argued to determine student engagement in robotics education, despite disagreements among pedagogical traits. Although Jung and Won ( 2018 ) provided information of teaching approaches applied in robotics education, they did not offer critical discussion on how those approaches were formed between robots and the teaching disciplines.

On the other hand, Conde et. al. ( 2021 ) identified PBL as the most common learning approach in their study by reviewing 54 papers from 2006 to 2019. Furthermore, the studies by Çetin and Demircan ( 2020 ) and Tselegkaridis and Sapounidis ( 2021 ) focused on the types of robots used in STEM education and reviewed 23 and 17 papers, respectively. Again, these studies touted learning engagement as a positive outcome, and disregarded the different perspectives of robot use in educational settings on students’ academic performance and cognition. More recently, a meta-analysis by Zhang et. al. ( 2021 ) focused on the effects of robotics on students’ computational thinking and their attitudes toward STEM learning. In addition, a systematic review by Atman Uslu et. al. ( 2022 ) examined the use of educational robotics and robots in learning.

So far, the review study conducted by Atman Uslu et. al. ( 2022 ) could be the only study that has attempted to clarify some of the criticisms of using educational robots by reviewing the studies published from 2006 to 2019 in terms of their research issues (e.g., interventions, interactions, and perceptions), theoretical models, and the roles of robots in educational settings. However, they failed to take into account several important features of robots in education research, such as thematic subjects and educational objectives, for instance, whether robot-based learning could enhance students’ competence of constructing new knowledge, or whether robots could bring either a motivational facet or creativity to pedagogy to foster students’ learning outcomes. These are essential in investigating the trends of technology-based learning research as well as the role of technology in education as a review study is aimed to offer a comprehensive discussion which derived from various angles and dimensions. Moreover, the role of robots in STEM education was generally ignored in the previous review studies. Hence, there is still a need for a comprehensive understanding of the role of robotics in STEM education and research trends (e.g., research issues, interaction issues, and application issues) so as to provide researchers and practitioners with valuable references. That is, our study can remedy the shortcomings of previous reviews (Additional file 1 ).

The above comments demonstrate how previous scholars have understood what they call “the effectiveness of robotics in STEM education” in terms of innovative educational tools. In other words, despite their useful findings and ongoing recommendations, there has not been a thorough investigation of how robots are widely used from all angles. Furthermore, the results of existing review studies have been less than comprehensive in terms of the potential role of robotics in R-STEM education after taking into account various potential dimensions based on the technology-based model that we propose in this study.

The studies in this review were selected from the literature on the Web of Science, our sole database due to its rigorous journal research and qualified studies (e.g., Huang et al., 2022 ), discussing the adoption of R-STEM education, and the data collection procedures for this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al., 2009 ) as referred to by prior studies (e.g., Chen et al., 2021a , 2021b ; García-Martínez et al., 2020 ). Considering publication quality, previous studies (Fu & Hwang, 2018 ; Martín-Páez et al., 2019 ) suggested using Boolean expressions to search Web of Science databases. The search terms for “robot” are “robot” or “robotics” or “robotics” or “Lego” (Spolaôr & Benitti, 2017 ). According to Martín-Páez et. al. ( 2019 ), expressions for STEM education include “STEM” or “STEM education” or “STEM literacy” or “STEM learning” or “STEM teaching” or “STEM competencies”. These search terms were entered into the WOS database to search only for SSCI papers due to its wide recognition as being high-quality publications in the field of educational technology. As a result, 165 papers were found in the database. The search was then restricted to 2012–2021 as suggested by Hwang and Tsai ( 2011 ). In addition, the number of papers was reduced to 131 by selecting only publications of the “article” type and those written in “English”. Subsequently, we selected the category “education and educational research” which reduced the number to 60 papers. During the coding analysis, the two coders screened out 21 papers unrelated to R-STEM education. The coding result had a Kappa coefficient of 0.8 for both coders (Cohen, 1960 ). After the screening stage, a final total of 39 articles were included in this study, as shown in Fig.  1 . Also, the selected papers are marked with an asterisk in the reference list and are listed in Appendixes 1 and 2 .

figure 1

PRISMA procedure for the selection process

Theoretical model, data coding, and analysis

This study comprised content analysis using a coding scheme to provide insights into different aspects of the studies in question (Chen et al., 2021a , 2021b ; Martín-Páez et al., 2019 ). The coding scheme adopted the conceptual framework proposed by Lin and Hwang ( 2019 ), comprising “STEM environments”, “learners”, and “robots”, as shown in Fig.  2 . Three issues were identified:

In terms of research issues, five dimensions were included: “location”, “sample size”, “duration of intervention”, (Zhong & Xia, 2020 ) “research methods”, (Johnson & Christensen, 2000 ) and “research foci”. (Hynes et al., 2017 ; Spolaôr & Benitti, 2017 ).

In terms of interaction issues, three dimensions were included: “participants”, (Hwang & Tsai, 2011 ), “roles of the robot”, and “types of robot” (Taylor, 1980 ).

In terms of application, five dimensions were included, namely “dominant STEM disciplines”, “integration of robot and STEM” (Martín‐Páez et al., 2019 ), “contribution to STEM disciplines”, “pedagogical intervention”, (Spolaôr & Benitti, 2017 ) and “educational objectives” (Anwar et al., 2019 ). Table 1 shows the coding items in each dimension of the investigated issues.

figure 2

Model of R-STEM education theme framework

Figure  3 shows the distribution of the publications selected from 2012 to 2021. The first two publications were found in 2012. From 2014 to 2017, the number of publications steadily increased, with two, three, four, and four publications, respectively. Moreover, R-STEM education has been increasingly discussed within the last 3 years (2018–2020) with six, three, and ten publications, respectively. The global pandemic in the early 2020s could have affected the number of papers published, with only five papers in 2021. This could be due to the fact that most robot-STEM education research is conducted in physical classroom settings.

figure 3

Number of publications on R-STEM education from 2012 to 2021

Table 2 displays the journals in which the selected papers were published, the number of papers published in each journal, and the journal’s impact factor. It can be concluded that most of the papers on R-STEM education research were published in the Journal of Science Education and Technology , and the International Journal of Technology and Design Education , with six papers, respectively.

Research issues

The geographic distribution of the reviewed studies indicated that more than half of the studies were conducted in the United States (53.8%), while Turkey and China were the location of five and three studies, respectively. Taiwan, Canada, and Italy were indicated to have two studies each. One study each was conducted in Australia, Mexico, and the Netherlands. Figure  4 shows the distribution of the countries where the R-STEM education was conducted.

figure 4

Locations where the studies were conducted ( N  = 39)

Sample size

Regarding sample size, there were four most common sample sizes for the selected period (2012–2021): greater than 80 people (28.21% or 11 out of 39 studies), between 41 and 60 (25.64% or 10 out of 39 studies), 1 to 20 people (23.08% or 9 out of 39), and between 21 and 40 (20.51% or 8 out of 39 studies). The size of 61 to 80 people (2.56% or 1 out of 39 studies) was the least popular sample size (see Fig.  5 ).

figure 5

Sample size across the studies ( N  = 39)

Duration of intervention

Regarding the duration of the study (see Fig.  6 ), experiments were mostly conducted for less than or equal to 4 weeks (35.9% or 14 out of 39 studies). This was followed by less than or equal to 8 weeks (25.64% or 10 out of 39 studies), less than or equal to 6 months (20.51% or 8 out 39 studies), less than or equal to 12 months (10.26% or 4 out of 39 studies), while less than or equal to 1 day (7.69% or 3 out of 39 studies) was the least chosen duration.

figure 6

Duration of interventions across the studies ( N  = 39)

Research methods

Figure  7 demonstrates the trends in research methods from 2012 to 2021. The use of questionnaires or surveys (35.9% or 14 out of 39 studies) and mixed methods research (35.9% or 14 out of 39 studies) outnumbered other methods such as experimental design (25.64% or 10 out of 39 studies) and system development (2.56% or 1 out of 39 studies).

figure 7

Frequency of each research method used in 2012–2021

Research foci

In these studies, research foci were divided into four aspects: cognition, affective, operational skill, and learning behavior. If the study involved more than one research focus, each issue was coded under each research focus.

In terms of cognitive skills, students’ learning performance was the most frequently measured (15 out of 39 studies). Six studies found that R-STEM education brought a positive result to learning performance. Two studies did not find any significant difference, while five studies showed mixed results or found that it depends. For example, Chang and Chen ( 2020 ) revealed that robots in STEM learning improved students’ cognition such as designing, electronic components, and computer programming.

In terms of affective skills, just over half of the reviewed studies (23 out of 39, 58.97%) addressed the students’ or teachers’ perceptions of employing robots in STEM education, of which 14 studies showed positive perceptions. In contrast, nine studies found mixed results. For instance, Casey et. al. ( 2018 ) determined students’ mixed perceptions of the use of robots in learning coding and programming.

Five studies were identified regarding operational skills by investigating students’ psychomotor aspects such as construction and mechanical elements (Pérez & López, 2019 ; Sullivan & Bers, 2016 ) and building and modeling robots (McDonald & Howell, 2012 ). Three studies found positive results, while two reported mixed results.

In terms of learning behavior, five out of 39 studies measured students’ learning behavior, such as students’ engagement with robots (Ma et al., 2020 ), students’ social behavior while interacting with robots (Konijn & Hoorn, 2020 ), and learner–parent interactions with interactive robots (Phamduy et al., 2017 ). Three studies showed positive results, while two found mixed results or found that it depends (see Table 3 ).

Interaction issues

Participants.

Regarding the educational level of the participants, elementary school students (33.33% or 13 studies) were the most preferred study participants, followed by high school students (15.38% or 6 studies). The data were similar for preschool, junior high school, in-service teachers, and non-designated personnel (10.26% or 4 studies). College students, including pre-service teachers, were the least preferred study participants. Interestingly, some studies involved study participants from more than one educational level. For example, Ucgul and Cagiltay ( 2014 ) conducted experiments with elementary and middle school students, while Chapman et. al. ( 2020 ) investigated the effectiveness of robots with elementary, middle, and high school students. One study exclusively investigated gifted and talented students without reporting their levels of education (Sen et al., 2021 ). Figure  8 shows the frequency of study participants between 2012 and 2021.

figure 8

Frequency of research participants in the selected period

The roles of robot

For the function of robots in STEM education, as shown in Fig.  9 , more than half of the selected articles used robots as tools (31 out of 39 studies, 79.49%) for which the robots were designed to foster students’ programming ability. For instance, Barker et. al. ( 2014 ) investigated students’ building and programming of robots in hands-on STEM activities. Seven out of 39 studies used robots as tutees (17.95%), with the aim of students and teachers learning to program. For example, Phamduy et. al. ( 2017 ) investigated a robotic fish exhibit to analyze visitors’ experience of controlling and interacting with the robot. The least frequent role was tutor (2.56%), with only one study which programmed the robot to act as tutor or teacher for students (see Fig.  9 ).

figure 9

Frequency of roles of robots

Types of robot

Furthermore, in terms of the types of robots used in STEM education, the LEGO MINDSTORMS robot was the most used (35.89% or 14 out of 39 studies), while Arduino was the second most used (12.82% or 5 out of 39 studies), and iRobot Create (5.12% or 2 out of 39 studies), and NAO (5.12% or 2 out of 39 studies) ranked third equal, as shown in Fig.  10 . LEGO was used to solve STEM problem-solving tasks such as building bridges (Convertini, 2021 ), robots (Chiang et al., 2020 ), and challenge-specific game boards (Leonard et al., 2018 ). Furthermore, four out of 36 studies did not specify the robots used in their studies.

figure 10

Frequency of types of robots used

Application issues

The dominant disciplines and the contribution to stem disciplines.

As shown in Table 4 , the most dominant discipline in R-STEM education research published from 2012 to 2021 was technology. Engineering, mathematics, and science were the least dominant disciplines. Programming was the most common subject for robotics contribution to the STEM disciplines (25 out of 36 studies, 64.1%), followed by engineering (12.82%), and mathematical method (12.82%). We found that interdisciplinary was discussed in the selected period, but in relatively small numbers. However, this finding is relevant to expose the use of robotics in STEM disciplines as a whole. For example, Barker et. al. ( 2014 ) studied how robotics instructional modules in geospatial and programming domains could be impacted by fidelity adherence and exposure to the modules. The dominance of STEM subjects based on robotics makes it necessary to study the way robotics and STEM are integrated into the learning process. Therefore, the forms of STEM integration are discussed in the following sub-section to report how teaching and learning of these disciplines can have learning goals in an integrated STEM environment.

Integration of robots and STEM

There are three general forms of STEM integration (see Fig.  11 ). Of these studies, robot-STEM content integration was commonly used (22 studies, 56.41%), in which robot activities had multiple STEM disciplinary learning objectives. For example, Chang and Chen ( 2020 ) employed Arduino in a robotics sailboat curriculum. This curriculum was a cross-disciplinary integration, the objectives of which were understanding sailboats and sensors (Science), the direction of motors and mechanical structures (Engineering), and control programming (Technology). The second most common form was supporting robot-STEM content integration (12 out of 39 studies, 30.76%). For instance, KIBO robots were used in the robotics activities where the mechanical elements content area was meaningfully covered in support of the main programming learning objectives (Sullivan & Bers, 2019 ). The least common form was robot-STEM context integration (5 out of 39 studies, 12.82%) which was implemented through the robot to situate the disciplinary content goals in another discipline’s practices. For example, Christensen et. al. ( 2015 ) analyzed the impact of an after-school program that offered robots as part of students’ challenges in a STEM competition environment (geoscience and programming).

figure 11

The forms of robot-STEM integration

Pedagogical interventions

In terms of instructional interventions, as shown in Fig.  12 , project-based learning (PBL) was the preferred instructional theory for using robots in R-STEM education (38.46% or 15 out 39 studies), with the aim of motivating students or robot users in the STEM learning activities. For example, Pérez and López ( 2019 ) argued that using low-cost robots in the teaching process increased students’ motivation and interest in STEM areas. Problem-based learning was the second most used intervention in this dimension (17.95% or 7 out of 39 studies). It aimed to improve students’ motivation by giving them an early insight into practical Engineering and Technology. For example, Gomoll et. al. ( 2017 ) employed robots to connect students from two different areas to work collaboratively. Their study showed the importance of robotic engagement in preliminary learning activities. Edutainment (12.82% or 5 out of 39 studies) was the third most used intervention. This intervention was used to bring together students and robots and to promote learning by doing. Christensen et. al. ( 2015 ) and Phamduy et. al. ( 2017 ) were the sample studies that found the benefits of hands-on and active learning engagement; for example, robotics competitions and robotics exhibitions could help retain a positive interest in STEM activities.

figure 12

The pedagogical interventions in R-STEM education

Educational objectives

As far as the educational objectives of robots are concerned (see Fig.  13 ), the majority of robots are used for learning and transfer skills (58.97% or 23 out of 39 studies) to enhance students’ construction of new knowledge. It emphasized the process of learning through inquiry, exploration, and making cognitive associations with prior knowledge. Chang and Chen’s ( 2020 ) is a sample study on how learning objectives promote students’ ability to transfer science and engineering knowledge learned through science experiments to design a robotics sailboat that could navigate automatically as a novel setting. Moreover, it also explicitly aimed to examine the hands-on learning experience with robots. For example, McDonald and Howell ( 2012 ) described how robots engaged with early year students to better understand the concepts of literacy and numeracy.

figure 13

Educational objectives of R-STEM education

Creativity and motivation were found to be educational objectives in R-STEM education for seven out of 39 studies (17.94%). It was considered from either the motivational facet of social trend or creativity in pedagogy to improve students’ interest in STEM disciplines. For instance, these studies were driven by the idea that employing robots could develop students’ scientific creativity (Guven et al., 2020 ), confidence and presentation ability (Chiang et al., 2020 ), passion for college and STEM fields (Meyers et al., 2012 ), and career choice (Ayar, 2015 ).

The general benefits of educational robots and the professional development of teachers were equally found in four studies each. The first objective, the general benefits of educational robotics, was to address those studies that found a broad benefit of using robots in STEM education without highlighting the particular focus. The sample studies suggested that robotics in STEM could promote active learning and improve students’ learning experience through social interaction (Hennessy Elliott, 2020 ) and collaborative science projects (Li et al., 2016 ). The latter, teachers’ professional development, was addressed by four studies (10.25%) to utilize robots to enhance teachers’ efficacy. Studies in this category discussed how teachers could examine and identify distinctive instructional approaches with robotics work (Bernstein et al., 2022 ), design meaningful learning instruction (Ryan et al., 2017 ) and lesson materials (Kim et al., 2015 ), and develop more robust cultural responsive self-efficacy (Leonard et al., 2018 ).

This review study was conducted using content analysis from the WOS collection of research on robotics in STEM education from 2012 to 2021. The findings are discussed under the headings of each research question.

RQ 1: In terms of research, what were the location, sample size, duration of intervention, research methods, and research foci of the R-STEM education research?

About half of the studies were conducted in North America (the USA and Canada), while limited studies were found from other continents (Europe and the Asia Pacific). This trend was identified in the previous study on robotics for STEM activities (Conde et al., 2021 ). Among 39 studies, 28 (71.79%) had fewer than 80 participants, while 11 (28.21%) had more than 80 participants. The intervention’s duration across the studies was almost equally divided between less than or equal to a month (17 out of 39 studies, 43.59%) and more than a month (22 out of 39 studies, 56.41%). The rationale behind the most popular durations is that these studies were conducted in classroom experiments and as conditional learning. For example, Kim et. al. ( 2018 ) conducted their experiments in a course offered at a university where it took 3 weeks based on a robotics module.

A total of four different research methodologies were adopted in the studies, the two most popular being mixed methods (35.89%) and questionnaires or surveys (35.89%). Although mixed methods can be daunting and time-consuming to conduct (Kucuk et al., 2013 ), the analysis found that it was one of the most used methods in the published articles, regardless of year. Chang and Chen ( 2022 ) embedded a mixed-methods design in their study to qualitatively answer their second research question. The possible reason for this is that other researchers prefer to use mixed methods as their research design. Their main research question was answered quantitatively, while the second and remaining research questions were reported through qualitative analysis (Casey et al., 2018 ; Chapman et al., 2020 ; Ma et al., 2020 ; Newton et al., 2020 ; Sullivan & Bers, 2019 ). Thus, it was concluded that mixed methods could lead to the best understanding and integration of research questions (Creswell & Clark, 2013 ; Creswell et al., 2003 ).

In contrast, system development was the least used compared to other study designs, as most studies used existing robotic systems. It should be acknowledged that the most common outcome we found was to enable students to understand these concepts as they relate to STEM subjects. Despite the focus on system development, the help of robotics was identified as increasing the success of STEM learning (Benitti, 2012 ). Because limited studies focused on system development as their primary purpose (1 out of 39 studies, 2.56%), needs analyses may ask whether the mechanisms, types, and challenges of robotics are appropriate for learners. Future research will need further design and development of personalized robots to fill this part of the research gap.

About half of the studies (23 studies, 58.97%) were focused on investigating the effectiveness of robots in STEM learning, primarily by collecting students’ and teachers’ opinions. This result is more similar to Belpaeme et al. ( 2018 ) finding that users’ perceptions were common measures in studies on robotics learning. However, identifying perceptions of R-STEM education may not help us understand exactly how robots’ specific features afford STEM learning. Therefore, it is argued that researchers should move beyond such simple collective perceptions in future research. Instead, further studies may compare different robots and their features. For instance, whether robots with multiple sensors, a sensor, or without a sensor could affect students’ cognitive, metacognitive, emotional, and motivational in STEM areas (e.g., Castro et al., 2018 ). Also, there could be instructional strategies embedded in R-STEM education that can lead students to do high-order thinking, such as problem-solving with a decision (Özüorçun & Bicen, 2017 ), self-regulated and self-engagement learning (e.g., Li et al., 2016 ). Researchers may also compare the robotics-based approach with other technology-based approaches (e.g., Han et al., 2015 ; Hsiao et al., 2015 ) in supporting STEM learning.

RQ 2: In terms of interaction, what were the participants, roles of the robots, and types of robots of the R-STEM education research?

The majority of reviewed studies on R-STEM education were conducted with K-12 students (27 studies, 69.23%), including preschool, elementary school, junior, and high school students. There were limited studies that involved higher education students and teachers. This finding is similar to the previous review study (Atman Uslu et al., 2022 ), which found a wide gap among research participants between K-12 students and higher education students, including teachers. Although it is unclear why there were limited studies conducted involving teachers and higher education students, which include pre-service teachers, we are aware of the critical task of designing meaningful R-STEM learning experiences which is likely to require professional development. In this case, both pre- and in-service teachers could examine specific objectives, identify topics, test the application, and design potential instruction to align well with robots in STEM learning (Bernstein et al., 2022 ). Concurrently, these pedagogical content skills in R-STEM disciplines might not be taught in the traditional pre-service teacher education and particular teachers’ development program (Huang et al., 2022 ). Thus, it is recommended that future studies could be conducted to understand whether robots can improve STEM education for higher education students and teachers professionally.

Regarding the role of robots, most were used as learning tools (31 studies, 79.48%). These robots are designed to have the functional ability to command or program some analysis and processing (Taylor, 1980 ). For example, Leonard et. al. ( 2018 ) described how pre-service teachers are trained in robotics activities to facilitate students’ learning of computational thinking. Therefore, robots primarily provide opportunities for learners to construct knowledge and skills. Only one study (2.56%), however, was found to program robots to act as tutors or teachers for students. Designing a robot-assisted system has become common in other fields such as language learning (e.g., Hong et al., 2016 ; Iio et al., 2019 ) and special education (e.g., Özdemir & Karaman, 2017 ) where the robots instruct the learning activities for students. In contrast, R-STEM education has not looked at the robot as a tutor, but has instead focused on learning how to build robots (Konijn & Hoorn, 2020 ). It is argued that robots with features as human tutors, such as providing personalized guidance and feedback, could assist during problem-solving activities (Fournier-Viger et al., 2013 ). Thus, it is worth exploring in what teaching roles the robot will work best as a tutor in STEM education.

When it comes to types of robots, the review found that LEGO dominated robots’ employment in STEM education (15 studies, 38.46%), while the other types were limited in their use. It is considered that LEGO tasks are more often associated with STEM because learners can be more involved in the engineering or technical tasks. Most researchers prefer to use LEGO in their studies (Convertini, 2021 ). Another interesting finding is about the cost of the robots. Although robots are generally inexpensive, some products are particularly low-cost and are commonly available in some regions (Conde et al., 2021 ). Most preferred robots are still considered exclusive learning tools in developing countries and regions. In this case, only one study offered a low-cost robot (Pérez & López, 2019 ). This might be a reason why the selected studies were primarily conducted in the countries and continents where the use of advanced technologies, such as robots, is growing rapidly (see Fig.  4 ). Based on this finding, there is a need for more research on the use of low-cost robots in R-STEM instruction in the least developed areas or regions of the world. For example, Nel et. al. ( 2017 ) designed a STEM program to build and design a robot which exclusively enabling students from low-income household to participate in the R-STEM activities.

RQ 3: In terms of application, what were the dominant STEM disciplines, contribution to STEM disciplines, integration of robots and STEM, pedagogical interventions, and educational objectives of the R-STEM research?

While Technology and Engineering are the dominant disciplines, this review found several studies that directed their research to interdisciplinary issues. The essence of STEM lies in interdisciplinary issues that integrate one discipline into another to create authentic learning (Hansen, 2014 ). This means that some researchers are keen to develop students’ integrated knowledge of Science, Technology, Engineering, and Mathematics (Chang & Chen, 2022 ; Luo et al., 2019 ). However, Science and Mathematics were given less weight in STEM learning activities compared to Technology and Engineering. This issue has been frequently reported as a barrier to implementing R-STEM in the interdisciplinary subject. Some reasons include difficulties in pedagogy and classroom roles, lack of curriculum integration, and a limited opportunity to embody one learning subject into others (Margot & Kettler, 2019 ). Therefore, further research is encouraged to treat these disciplines equally, so is the way of STEM learning integration.

The subject-matter results revealed that “programming” was the most common research focus in R-STEM research (25 studies). Researchers considered programming because this particular topic was frequently emphasized in their studies (Chang & Chen, 2020 , 2022 ; Newton et al., 2020 ). Similarly, programming concepts were taught through support robots for kindergarteners (Sullivan & Bers, 2019 ), girls attending summer camps (Chapman et al., 2020 ), and young learners with disabilities (Lamptey et al., 2021 ). Because programming simultaneously accompanies students’ STEM learning, we believe future research can incorporate a more dynamic and comprehensive learning focus. Robotics-based STEM education research is expected to encounter many interdisciplinary learning issues.

Researchers in the reviewed studies agreed that the robot could be integrated with STEM learning with various integration forms. Bryan et. al. ( 2015 ) argued that robots were designed to develop multiple learning goals from STEM knowledge, beginning with an initial learning context. It is parallel with our finding that robot-STEM content integration was the most common integration form (22 studies, 56.41%). In this form, studies mainly defined their primary learning goals with one or more anchor STEM disciplines (e.g., Castro et al., 2018 ; Chang & Chen, 2020 ; Luo et al., 2019 ). The learning goals provided coherence between instructional activities and assessments that explicitly focused on the connection among STEM disciplines. As a result, students can develop a deep and transferable understanding of interdisciplinary phenomena and problems through emphasizing the content across disciplines (Bryan et al., 2015 ). However, the findings on learning instruction and evaluation in this integration are inconclusive. A better understanding of the embodiment of learning contexts is needed, for instance, whether instructions are inclusive, socially relevant, and authentic in the situated context. Thus, future research is needed to identify the quality of instruction and evaluation and the specific characteristics of robot-STEM integration. This may place better provision of opportunities for understanding the form of pedagogical content knowledge to enhance practitioners’ self-efficacy and pedagogical beliefs (Chen et al., 2021a , 2021b ).

Project-based learning (PBL) was the most used instructional intervention with robots in R-STEM education (15 studies, 38.46%). Blumenfeld et al. ( 1991 ) credited PBL with the main purpose of engaging students in investigating learning models. In the case of robotics, students can create robotic artifacts (Spolaôr & Benitti, 2017 ). McDonald and Howell ( 2012 ) used robotics to develop technological skills in lower grades. Leonard et. al. ( 2016 ) used robots to engage and develop students’ computational thinking strategies in another example. In the aforementioned study, robots were used to support learning content in informal education, and both teachers and students designed robotics experiences aligned with the curriculum (Bernstein et al., 2022 ). As previously mentioned, this study is an example of how robots can cover STEM content from the learning domain to support educational goals.

The educational goal of R-STEM education was the last finding of our study. Most of the reviewed studies focused on learning and transferable skills as their goals (23 studies, 58.97%). They targeted learning because the authors investigated the effectiveness of R-STEM learning activities (Castro et al., 2018 ; Convertini, 2021 ; Konijn & Hoorn, 2020 ; Ma et al., 2020 ) and conceptual knowledge of STEM disciplines (Barak & Assal, 2018 ; Gomoll et al., 2017 ; Jaipal-Jamani & Angeli 2017 ). They targeted transferable skills because they require learners to develop individual competencies in STEM skills (Kim et al., 2018 ; McDonald & Howell, 2012 ; Sullivan & Bers, 2016 ) and to master STEM in actual competition-related skills (Chiang et al., 2020 ; Hennessy Elliott, 2020 ).

Conclusions and implications

The majority of the articles examined in this study referred to theoretical frameworks or certain applications of pedagogical theories. This finding contradicts Atman Uslu et. al. ( 2022 ), who concluded that most of the studies in this domain did not refer to pedagogical approaches. Although we claim the employment pedagogical frameworks in the examined articles exist, those articles primarily did not consider a strict instructional design when employing robots in STEM learning. Consequently, the discussions in the studies did not include how the learning–teaching process affords students’ positive perceptions. Therefore, both practitioners and researchers should consider designing learning instruction using robots in STEM education. To put an example, the practitioners may regard students’ zone of proximal development (ZPD) when employing robot in STEM tasks. Giving an appropriate scaffolding and learning contents are necessary for them to enhance their operational skills, application knowledge and emotional development. Although the integration between robots and STEM education was founded in the reviewed studies, it is worth further investigating the disciplines in which STEM activities have been conducted. This current review found that technology and engineering were the subject areas of most concern to researchers, while science and mathematics did not attract as much attention. This situation can be interpreted as an inadequate evaluation of R-STEM education. In other words, although those studies aimed at the interdisciplinary subject, most assessments and evaluations were monodisciplinary and targeted only knowledge. Therefore, it is necessary to carry out further studies in these insufficient subject areas to measure and answer the potential of robots in every STEM field and its integration. Moreover, the broadly consistent reporting of robotics generally supporting STEM content could impact practitioners only to employ robots in the mainstream STEM educational environment. Until that point, very few studies had investigated the prominence use of robots in various and large-scale multidiscipline studies (e.g., Christensen et al., 2015 ).

Another finding of the reviewed studies was the characteristic of robot-STEM integration. Researchers and practitioners must first answer why and how integrated R-STEM could be embodied in the teaching–learning process. For example, when robots are used as a learning tool to achieve STEM learning objectives, practitioners are suggested to have application knowledge. At the same time, researchers are advised to understand the pedagogical theories so that R-STEM integration can be flexibly merged into learning content. This means that the learning design should offer students’ existing knowledge of the immersive experience in dealing with robots and STEM activities that assist them in being aware of their ideas, then building their knowledge. In such a learning experience, students will understand the concept of STEM more deeply by engaging with robots. Moreover, demonstration of R-STEM learning is not only about the coherent understanding of the content knowledge. Practitioners need to apply both flexible subject-matter knowledge (e.g., central facts, concepts and procedures in the core concept of knowledge), and pedagogical content knowledge, which specific knowledge of approaches that are suitable for organizing and delivering topic-specific content, to the discipline of R-STEM education. Consequently, practitioners are required to understand the nature of robots and STEM through the content and practices, for example, taking the lead in implementing innovation through subject area instruction, developing collaboration that enriches R-STEM learning experiences for students, and being reflective practitioners by using students’ learning artifacts to inform and revise practices.

Limitations and recommendations for future research

Overall, future research could explore the great potential of using robots in education to build students’ knowledge and skills when pursuing learning objectives. It is believed that the findings from this study will provide insightful information for future research.

The articles reviewed in this study were limited to journals indexed in the WOS database and R-STEM education-related SSCI articles. However, other databases and indexes (e.g., SCOPUS, and SCI) could be considered. In addition, the number of studies analyzed was relatively small. Further research is recommended to extend the review duration to cover the publications in the coming years. The results of this review study have provided directions for the research area of STEM education and robotics. Specifically, robotics combined with STEM education activities should aim to foster the development of creativity. Future research may aim to develop skills in specific areas such as robotics STEM education combined with the humanities, but also skills in other humanities disciplines across learning activities, social/interactive skills, and general guidelines for learners at different educational levels. Educators can design career readiness activities to help learners build self-directed learning plans.

Availability of data and materials

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

Science, technology, engineering, and mathematics

Robotics-based STEM

Project-based learning

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Acknowledgements

The authors would like to express their gratefulness to the three anonymous reviewers for providing their precious comments to refine this manuscript.

This study was supported by the Ministry of Science and Technology of Taiwan under contract numbers MOST-109-2511-H-011-002-MY3 and MOST-108-2511-H-011-005-MY3; National Science and Technology Council (TW) (NSTC 111-2410-H-031-092-MY2); Soochow University (TW) (111160605-0014). Any opinions, findings, conclusions, and/or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of Ministry of Science and Technology of Taiwan.

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Department of English Language and Literature, Soochow University, Q114, No. 70, Linhsi Road, Shihlin District, Taipei, 111, Taiwan

Mei-Rong Alice Chen

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Contributions

DD, MR and GJ conceptualized the study. MR wrote the outline and DD wrote draft. DD, MR and GJ contributed to the manuscript through critical reviews. DD, MR and GJH revised the manuscript. DD, MR and GJ finalized the manuscript. DD edited the manuscript. MR and GJ monitored the project and provided adequate supervision. DD, MR and JC contributed with data collection, coding, analyses and interpretation. All authors read and approved the final manuscript.

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Correspondence to Mei-Rong Alice Chen .

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Supplementary Information

Additional file 1..

Coded papers.

Appendix 1. Summary of selected studies from the angle of research issue

#

Authors

Dimension

Location

Sample size

Duration of intervention

Research methods

Research foci

1

Convertini ( )

Italy

21–40

≤ 1 day

Experimental design

Problem solving, collaboration or teamwork, and communication

2

Lamptey et. al. ( )

Canada

41–60

≤ 8 weeks

Mixed method

Satisfaction or interest, and learning perceptions

3

Üçgül and Altıok ( )

Turkey

41–60

≤ 1 day

Questionnaire or survey

Attitude and motivation, learning perceptions

4

Sen et. al. ( )

Turkey

1–20

≤ 4 weeks

Experimental design

Problem solving, critical thinking, logical thinking, creativity, collaboration or teamwork, and communication

5

Stewart et. al. ( )

USA

> 80

≤ 6 months

Mixed method

Higher order thinking skills, problem-solving, technology acceptance, attitude and motivation, and learning perceptions

6

Bernstein et. al. ( )

USA

1–20

≤ 1 day

Questionnaire or survey

Attitude and motivation, and learning perceptions

7

Chang and Chen ( )

Taiwan

41–60

≤ 8 weeks

Mixed method

Learning performance, problem-solving, satisfaction or interest, and operational skill

8

Chang and Chen ( )

Taiwan

41–60

≤ 8 weeks

Experimental design

Learning perceptions, and operational skill

9

Chapman et al. ( )

USA

> 80

≤ 8 weeks

Mixed method

Learning performance, and learning perceptions

10

Chiang et. al. ( )

China

41–60

≤ 4 weeks

Questionnaire or survey

Creativity, and self-efficacy and confidence

11

Guven et. al. ( )

Turkey

1–20

≤ 6 months

Mixed method

Creativity, technology acceptance, attitude and motivation, self-efficacy or confidence, satisfaction or interest, and learning perception

12

Hennessy Elliott ( )

USA

1–20

≤ 12 months

Experimental design

Collaboration, communication, and preview situation

13

Konijn and Hoorn ( )

Netherlands

41–60

≤ 4 weeks

Experimental design

Learning performance, and learning behavior

14

Ma et. al. ( )

China

41–60

≤ 6 months

Mixed method

Learning performance, learning perceptions, and learning behavior

15

Newton et. al. ( )

USA

> 80

≤ 6 months

Mixed method

Attitude and motivation, and self-efficacy and confidence

16

Luo et. al. ( )

USA

41–60

≤ 4 weeks

Questionnaire or survey

Technology acceptance, attitude and motivation, and self-efficacy

17

Pérez and López ( )

Mexico

21–40

≤ 6 months

System development

Operational skill

18

Sullivan and Bers ( )

USA

> 80

≤ 8 weeks

Mixed method

Attitude and motivation, satisfaction or interest, and learning behavior

19

Barak and Assal ( )

Israel

21–40

≤ 6 months

Mixed method

Learning performance, technology acceptance, self-efficacy, and satisfaction or interest

20

Castro et. al. ( )

Italy

> 80

≤ 8 weeks

Questionnaire or survey

Learning performance, and self-efficacy

21

Casey et. al. ( )

USA

> 80

≤ 12 months

Questionnaire or survey

Learning satisfaction

22

Kim et. al. ( )

USA

1–20

≤ 4 weeks

Questionnaire or survey

Problem solving, and preview situation

23

Leonard et. al. ( )

USA

41–60

≤ 12 months

Questionnaire or survey

Learning performance, self-efficacy, and learning perceptions

24

Taylor ( )

USA

1–20

≤ 1 day

Experimental design

Learning performance, and preview situation

25

Gomoll et. al. ( )

USA

21–40

≤ 8 weeks

Experimental design

Problem solving, collaboration, communication

26

Jaipal-Jamani and Angeli ( )

Canada

21–40

≤ 4 weeks

Mixed method

Learning performance, self-efficacy, and satisfaction or interest

27

Phamduy et. al. ( )

USA

> 80

≤ 4 weeks

Mixed method

Satisfaction or interest, and learning behavior

28

Ryan et. al. ( )

USA

1–20

≤ 12 months

Questionnaire or survey

Learning perceptions

29

Gomoll et. al. ( )

USA

21–40

≤ 6 months

Experimental design

Satisfaction or interest, and learning perceptions

30

Leonard et. al. ( )

USA

61–80

≤ 4 weeks

Mixed method

Attitude and motivation, and self-efficacy

31

Li et. al. ( )

China

21–40

≤ 8 weeks

Experimental design

Learning performance, and problem-solving,

32

Sullivan and Bers ( )

USA

41–60

≤ 8 weeks

Experimental design

Learning performance, and operational skill

33

Ayar ( )

Turkey

> 80

≤ 4 weeks

Questionnaire or survey

Attitude and motivation, satisfaction or interest, and learning perceptions

34

Christensen et. al. ( )

USA

> 80

 ≤ 6 months

Questionnaire or survey

Technology acceptance, satisfaction or interest, and learning perceptions

35

Kim et al. ( )

USA

1–20

≤ 4 weeks

Mixed method

Learning performance, satisfaction or interest, and learning perceptions

36

Barker et. al. ( )

USA

21–40

≤ 4 weeks

Questionnaire or survey

Technology acceptance, attitude and motivation, and learning perceptions

37

Ucgul and Cagiltay ( )

Turkey

41–60

≤ 4 weeks

Questionnaire or survey

Learning performance, satisfaction or interest, and learning perceptions

38

McDonald and Howell ( )

Australia

1–20

≤ 8 weeks

Mixed method

Learning performance, operational skills, and learning behavior

39

Meyers et. al. ( )

USA

> 80

≤ 4 weeks

Questionnaire or survey

Learning perceptions

Appendix 2. Summary of selected studies from the angles of interaction and application

#

Authors

Interaction

Application

Participants

Role of robot

Types of robot

Dominant STEM discipline

Contribution to STEM

Integration of robot and STEM

Pedagogical intervention

Educational objectives

1

Convertini ( )

Preschool or Kindergarten

Tutee

LEGO (Mindstorms)

Engineering

Structure and construction

Context integration

Active construction

Learning and transfer skills

2

Lamptey et. al. ( )

Non-specified

Tool

LEGO (Mindstorms)

Technology

Programming

Supporting content integration

Problem-based learning

Learning and transfer skills

3

Üçgül and Altıok ( )

Junior high school students

Tool

LEGO (Mindstorms)

Technology

Programming

Content integration

Project-based learning

Creativity and motivation

4

Sen et. al. ( )

Others (gifted and talented students)

Tutee

LEGO (Mindstorms)

Technology

Programming, and Mathematical methods

Supporting content integration

Problem-based learning

Learning and transfer skills

5

Stewart et. al. ( )

Elementary school students

Tool

Botball robot

Technology

Programming, and power and dynamical system

Content integration

Project-based learning

Learning and transfer skills

6

Bernstein et. al. ( )

In-service teachers

Tool

Non-specified

Science

Biomechanics

Content integration

Project-based learning

Teachers’ professional development

7

Chang and Chen ( )

High school students

Tool

Arduino

Interdisciplinary

Basic Physics, Programming, Component design, and mathematical methods

Content integration

Project-based learning

Learning transfer and skills

8

Chang and Chen ( )

High school students

Tool

Arduino

Interdisciplinary

Basic Physics, Programming, Component design, and mathematical methods

Content integration

Project-based learning

Learning transfer and skills

9

Chapman et. al. ( )

Elementary, middle, and high school students

Tool

LEGO (Mindstorms) and Maglev trains

Engineering

Engineering

Content integration

Engaged learning

Learning transfer and skills

10

Chiang et. al. ( )

Non-specified

Tool

LEGO (Mindstorms)

Technology

Non-specified

Context integration

Edutainment

Creativity and motivation

11

Guven et. al. ( )

Elementary school students

Tutee

Arduino

Technology

Programming

Content integration

Constructivism

Creativity and motivation

12

Hennessy Elliott ( )

Students and teachers

Tool

Non-specified

Technology

Non-specified

Supporting content integration

Collaborative learning

General benefits of educational robotics

13

Konijn and Hoorn ( )

Elementary school students

Tutor

Nao robot

Mathematics

Mathematical methods

Supporting content integration

Engaged learning

Learning and transfer skills

14

Ma et. al. ( )

Elementary school students

Tool

Microduino and Makeblock

Engineering

Non-specified

Content integration

Experiential learning

Learning and transfer skills

15

Newton et. al. ( )

Elementary school students

Tool

LEGO (Mindstorms)

Technology

Programming

Supporting content integration

Active construction

Learning and transfer skills

16

Luo et. al. ( )

Junior high or middle school

Tool

Vex robots

Interdisciplinary

Programming, Engineering, and Mathematics

Content integration

Constructivism

General benefits of educational robots

17

Pérez and López ( )

High school students

Tutee

Arduino

Engineering

Programming, and mechanics

Content integration

Project-based learning

Learning and transfer skills

18

Sullivan and Bers ( )

Kindergarten and Elementary school students

Tool

KIBO robots

Technology

Programming

Context integration

Project-based learning

Learning and transfer skills

19

Barak and Assal ( )

High school students

Tool

Non-specified

Technology

Programming, mathematical methods

Content integration

Problem-based learning

Learning and transfer skills

20

Castro et. al. ( )

Lower secondary

Tool

Bee-bot

Technology

Programming

Content integration

Problem-based learning

Learning and transfer skills

21

Casey et. al. ( )

Elementary school students

Tool

Roamers robot

Technology

Programming

Content integration

Metacognitive learning

Learning and transfer skills

22

Kim et. al. ( )

Pre-service teachers

Tool

Non-specified

Technology

Programming

Supporting content integration

Problem-based learning

Learning and transfer skills

23

Leonard et. al. ( )

In-service teachers

Tool

LEGO (Mindstorms)

Technology

Programming

Supporting content integration

Project-based learning

Teachers’ professional development

24

Taylor ( )

Kindergarten and elementary school students

Tool

Dash robot

Technology

Programming,

Content integration

Problem-based learning

Learning and transfer skills

25

Gomoll et. al. ( )

Middle school students

Tool

iRobot create

Technology

Programming, and structure and construction

Content integration

Problem-based learning

Learning and transfer skills

26

Jaipal-Jamani and Angeli ( )

Pre-service teachers

Tool

LEGO WeDo

Technology

Programming

Supporting content integration

Project-based learning

Learning and transfer skills

27

Phamduy et. al. ( )

Non-specified

Tutee

Arduino

Science

Biology

Context integration

Edutainment

Diversity and broadening participation

28

Ryan et. al. ( )

In-service teachers

Tool

LEGO (Mindstorms)

Engineering

Engineering

Content integration

Constructivism

Teacher’s professional development

29

Gomoll et. al. ( )

Non-specified

Tool

iRobot create

Technology

Programming

Content integration

Project-based learning

Learning and transfer skill

30

Leonard et. al. ( )

Middle school students

Tool

LEGO (Mindstorms)

Technology

Programming

Content integration

Project-based learning

Learning and transfer skill

31

Li et. al. ( )

Elementary school students

Tool

LEGO Bricks

Engineering

Structure and construction

Supporting content integration

Project-based learning

General benefits of educational robotics

32

Sullivan and Bers ( )

Kindergarten and Elementary school students

Tool

Kiwi Kits

Engineering

Digital signal process

Content integration

Project-based learning

Learning and transfer skill

33

Ayar ( )

High school students

Tool

Nao robot

Engineering

Component design

Content integration

Edutainment

Creativity and 34motivation

34

Christensen et. al. ( )

Middle and high school students

Tutee

Non-specified

Engineering

Engineering

Context integration

Edutainment

Creativity and motivation

35

Kim et. al. ( )

Pre-service teachers

Tool

RoboRobo

Technology

Programming

Supporting content integration

Engaged learning

Teachers’ professional development

36

Barker et. al. ( )

In-service teachers

Tool

LEGO (Mindstorms)

Technology

Geography information system, and programming

Supporting content integration

Constructivism

Creativity and motivation

37

Ucgul and Cagiltay ( )

Elementary and Middle school students

Tool

LEGO (Mindstorms)

Technology

Programming, mechanics, and mathematics

Content integration

Project-based learning

General benefits of educational robots

38

McDonald and Howell ( )

Elementary school students

Tool

LEGO WeDo

Technology

Programming, and students and construction

Content integration

Project-based learning

Learning and transfer skills

39

Meyers et. al. ( )

Elementary school students

Tool

LEGO (Mindstorms)

Engineering

Engineering

Supporting content integration

Edutainment

Creativity and motivation

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Darmawansah, D., Hwang, GJ., Chen, MR.A. et al. Trends and research foci of robotics-based STEM education: a systematic review from diverse angles based on the technology-based learning model. IJ STEM Ed 10 , 12 (2023). https://doi.org/10.1186/s40594-023-00400-3

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Soft Robotics: A Systematic Review and Bibliometric Analysis

Dan-mihai rusu.

1 Mechatronics and Machine Dynamics Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania

Silviu-Dan Mândru

Cristina-maria biriș.

2 Department of Industrial Machines and Equipment, Engineering Faculty, Lucian Blaga University of Sibiu, Victoriei 10, 550024 Sibiu, Romania

Olivia-Laura Petrașcu

Fineas morariu, alexandru ianosi-andreeva-dimitrova, associated data.

The data sets used in this study are available on request from the corresponding author.

In recent years, soft robotics has developed considerably, especially since the year 2018 when it became a hot field among current research topics. The attention that this field receives from researchers and the public is marked by the substantial increase in both the quantity and the quality of scientific publications. In this review, in order to create a relevant and comprehensive picture of this field both quantitatively and qualitatively, the paper approaches two directions. The first direction is centered on a bibliometric analysis focused on the period 2008–2022 with the exact expression that best characterizes this field, which is “Soft Robotics”, and the data were taken from a series of multidisciplinary databases and a specialized journal. The second direction focuses on the analysis of bibliographic references that were rigorously selected following a clear methodology based on a series of inclusion and exclusion criteria. After the selection of bibliographic sources, 111 papers were part of the final analysis, which have been analyzed in detail considering three different perspectives: one related to the design principle (biologically inspired soft robotics), one related to functionality (closed/open-loop control), and one from a biomedical applications perspective.

1. Introduction

The field of soft robotics is scientifically considered a field of spectacular development from one year to the next, this being based on the potential that it has, namely, to offer other perspectives in the field of robotics and many others. What is spectacular is that the field of soft robotics, being relatively young and appearing as a term only in 2008, has gradually developed, reaching over 1000 scientific publications in databases such as Web of Science (WOS) and Scopus in the year 2022. Several aspects related to the history of soft robots were addressed in the review by Bao et al. [ 1 ]. Since the field of soft robotics is young, open, and outside of dogmatic restrictions in terms of manufacturing, modeling, and fields of use [ 2 ], this can introduce several ambiguities or confusions. One of these is related to the definition of soft robotics. In the specialized literature analyzed, many authors propose their definitions based on their research, but the soft robotics community has not reached a unanimously accepted definition that answers the question concerning what soft robotics is. That is why in this paper some of the definitions are accumulated, giving young or senior researchers a perspective on the mentioned question. The first such definition is: “ Soft robots are primarily composed of easily deformable matter such as fluids, gels, and elastomers that match the elastic and rheological properties of biological tissue and organs.” [ 2 ]. The following definition is provided by Rus et al.: “ We define soft robots as systems capable of autonomous behavior and which are primarily composed of materials with modules in the field of soft biological materials.” [ 3 ]. Alternatively, the definition from Panagiotis Polygerinos et al. states that a “ soft robot is appropriately named when the stresses it is subject to cause it to deform prior to damaging the class of objects for which it was designed (whether it be human or cantaloupe); we acknowledge that traditional robots can be thought of as soft when interacting with a harder object, such as a diamond. ” [ 4 ]. At the same time, the following definition was also offered: “ Soft robotics is the subject to study how to make use of the softness of an object or a piece of materials or a system for building a robot by satisfying a required softness to both its environment and its receiver.” [ 5 ]. There is also the definition from Liyu Wang et al.: “ We define soft-matter robotics as robotics that studies how deformation of soft matter can be exploited or controlled to achieve robotic functions.” [ 6 ]. These definitions of soft robotics contain similar aspects related to the source of inspiration, material, high compliance, and high deformability of soft robots. Considering the definitions above, one can be proposed that integrates all the aspects identified. A possible collective definition could be the following: soft robotics is a growing subfield of robotics that mainly draws inspiration from biological systems and uses materials with coefficients in the range of soft materials with high and continuous deformability so as to achieve specific robotic functions.

Over the years, in the soft robotics literature, several reviews have been published that address the field and focus on different specific application areas or reviews so as to create a comprehensive and precise picture. Based on the accelerated growth of scientific publications in recent years, the present paper responds to the need for centralization and provides an updated perspective of the achievements of recent years by generating a comprehensive view of the field. This paper represents a hybridization that approaches two categories of analysis. In the first part of the paper, a bibliometric analysis is carried out in which the evolution of the number of scientific publications from 2008 to July 2022 is identified alongside an analysis of the publications that considers aspects such as the most productive articles, journals, countries, and authors in this field, as well as the most cited scientific articles. The second part of the paper analyses the state of the art in the field of soft robotics from 2018 to July 2022, whereby the selection of articles is based on a clear methodology that is carried out in two stages due to the large number of articles found.

Considering the first part of the research, other reviews with bibliometric or scientometric analyses of soft robotics have been identified in the literature. This tool provides authors with a relevant method for mapping the evolution of the number of scientific publications over time in various fields. The first identified bibliometric analysis conducted in the field of soft robotics was that of Bao et al. [ 1 ], who retrieved data from the WOS database for studies published between 1990 and May 2017 using a range of keywords relevant to the field, which resulted in 1495 review and research articles being selected; in that paper numerous different aspects were analyzed, such as those related to productive countries, collaborations between countries, universities, journals, productive authors, and research areas contributing to the field. Another review that treats the field of soft robotics from a quantitative perspective is that of Yitong Zhou et al. [ 7 ], who conducted a scientometric analysis of studies published between 2010 and July 2021 (also from the WOS database) using a series of domain-specific keywords. From the search, 10504 results were obtained, and the researchers analyzed similar aspects to those in the analysis of Bao et al. In that paper, CiteSpace was used to make co-citation network maps. Another graph that highlights the evolution of the number of scientific publications is that of Laschi et al. [ 8 ], whose study was based on the Scopus database and publications between 2004 and 2016.

The second part of the paper, which qualitatively analyses the field of soft robotics, represents the state of the art in the field. The analysis of the field is based on 6400 research and review articles selected from four databases (WOS, ScienceDirect, IEEEXplore, and SpringerLink) with multidisciplinary character and the journal “ Soft Robotics ”. All these articles were obtained with an exact match for the search term “Soft Robotics” in the 2018–July 2022 timeframe. Due to the large number of results identified, the selection methodology was based on a set of clear inclusion and exclusion criteria, with the selection of relevant articles being carried out in two stages. After the first selection stage, 824 articles were selected based on the exclusion criteria. Following the second selection stage, 111 relevant articles were selected by applying the inclusion criteria that needed to be satisfied for articles to be part of the final domain analysis.

2. Bibliometric Analysis of the Field of Soft Robotics

2.1. selection methodology.

The bibliometric analysis considering the evolution of the number of publications is based on publications related to soft robotics between 2008 and July 2022. The year 2008 was not chosen by chance, as this was the year when the term soft robotics was widely adopted by the robotics community. For the graph regarding the mentioned evolution there were four databases used (WOS, ScienceDirect, IEEEXplore, and SpringerLink), as well as the specialized journal “ Soft Robotics ”. The data from the mentioned sources were retrieved with the exact search term “Soft Robotics”, which best characterizes the domain. Only reviews and research articles in English were selected. For the bibliometric analysis considering aspects such as authors, countries, and journals, the data were retrieved from the WOS Core Collection database with the same inclusion criteria as above.

2.2. Results

The first analysis carried out within the bibliometric study is related to the evolution of the number of publications ( Figure 1 ) in the field of soft robotics from the mentioned databases and the journal “ Soft Robotics ”. As a result of the analysis, 7646 publications were obtained. To avoid journals found in multiple databases, 35 journals that were duplicates were excluded from the analysis of the WOS database. This approach is an original one because, compared to other scientific sources, there is no such analysis in which the data are taken from several databases.

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Evolution of scientific publications in the 2008–July 2022 period with the exact search “Soft Robotics” on the Science Direct, WOS, IEEE Xplore, and SpringerLink databases and the “ Soft Robotics” journal.

The graph shows two curves that represent the annual evolution, which represents the results for each year from the four databases and the journal (blue line), and the cumulative evolution, which represents the summation of all the articles found each year from the four databases and the journal (orange line). The field of soft robotics started timidly with only a few articles in 2008 and continued with a weak evolution until 2012–2013 when the number of publications began to grow at a higher rate, though far from reaching 1000 articles. The increases in 2012–2015 are somewhat constant and from 2016 the domain begins to have a strong increase in the number of articles; in 2017 the domain accumulated more than 1000 articles. From 2016 to 2021, the number of articles grew significantly from year to year, which shows the interest of more and more researchers in this field. In 2021, the number of published articles reached approximately 2000, and this trend continued in 2022 with approximately 1500 articles being recorded by July 2022. What can be observed from the graph in Figure 1 is that an incredibly large number of publications were published in the 2018–2022 period. Publications from 2008–2017 represent 13.37% of the production of articles in the field, while those from 2018–2022 represent 87.63% of the entire 2008–2022 period.

The second analysis in the bibliometric study was conducted based on the WOS database, which is an international multidisciplinary database that gives the field of soft robots a global presence. It also provides researchers with a range of criteria for analysis according to their field of interest, ranking search results according to criteria selected by the user. After applying the criteria mentioned in the selection methodology section, a total of 3681 research articles and reviews were obtained from the WOS Core Collection database. Analyzing the 3681 articles according to the two types of documents selected as filters, research articles predominate with 3338 articles, representing 90.67%, and 343 review articles represent 9.32% of the total. This distribution of the number of articles represents a typical one, with review articles usually having a smaller number of publications. However, the field of soft robotics is continuously evolving. In a very short time window, as illustrated by Figure 1 , many new developments were documented by new research articles; as a consequence, many past reviews of the field have lost their edge. The ones that are still relevant approached the subject with a different methodology. Thus, the aim of this review article is to provide a fresh and valuable perspective.

As soft robotics is a multidisciplinary field [ 3 ], in recent years this feature has been further extended. Table 1 shows the top 10 WOS research areas ranked by the number of articles. The main category is “Materials Science Multidisciplinary”, which consists of 1335 publications representing 36.267% of the 3681 articles. A considerable amount of soft robotics features is based on material properties such as compliance, elasticity, and high and continuous deformability. The second significant research area is “Robotics”, with 1080 articles representing 29.340% of the 3681 results. A total of 650 papers that contributed to the field of soft robotics were from the “Nanoscience Nanotechnology” category. The research contribution indexed in the “Nanoscience Nanotechnology” category in the field of soft robotics addresses aspects related to materials, actuators, and sensors. The multidisciplinary nature of soft robotics also includes areas such as “Applied Physics”, “Chemistry”, and “Electrical Engineering”.

Top 10 research areas in WOS contributing to the field of soft robotics.

No.WOS CategoriesNumber of Publications% of 3681
1Materials Science Multidisciplinary133536.267%
2Robotics108029.340%
3Nanoscience Nanotechnology65017.658%
4Physics Applied54314.751%
5Chemistry Multidisciplinary49213.366%
6Chemistry Physical44011.953%
7Physics Condensed Matter3168.585%
8Instruments Instrumentation3018.177%
9Engineering Electrical Electronic2617.090%
10Polymer Science2145.814%

Considering the most productive journals publishing on soft robotics, Table 2 shows the top 10 journals in this area. The journal “ Soft Robotics ” ranks first with the highest number of articles published, namely 457. This journal is dedicated to this field and has published six issues of the journal every year since 2018. This journal accounts for 12.415% of the identified articles, which is a significant percentage. The “ ACS Applied Materials and Interfaces ” journal is the second-ranked journal with 179 publications (4.863%), which indicates a significant difference between the top two places. As “ ACS Applied Materials and Interfaces ” is not a soft robotics journal, it publishes specialized material articles. “ IEEE Robotics and Automation Letters ” was ranked in 3rd place and is a journal that is focused on robotics and automation, though it also publishes articles related to soft robotics. In the 4th place, the “ Advanced Materials ” journal focuses on materials and therefore publishes articles in the field of soft robotics from a materials perspective. Each journal has more than 100 articles published on soft robotics, representing more than 3% of the 3681 articles.

Top 10 journals that have published the most about soft robotics.

No.Publication TitleNumber of Publications% of 3681Impact Factor (2021–2022)
1 45712.415%7.784
2 1794.863%10.383
3 1313.559%4.321
4 1233.341%32.09
5 1102.988%19.92
6 972.635%4.331
7 962.608%8.856
8 792.146%3.585
9 731.983%7.298
10 601.630%2.956

Looking at the other positions, there is an alternation between material-focused journals and smart systems, robots, and AI. Referring to the impact factor of each journal, “ Advanced Materials ” has the highest impact factor (32.09) and “ Advanced Functional Materials ” also has a high impact factor (19.92), both journals being focused especially on materials. The robotics journal with the highest impact factor is “ Soft Robotics ” (IF 7.784), while it also has the highest contribution to the field in terms of the number of articles.

Table 3 identifies the 10 countries that made the most substantial contribution to soft robotics. More than 60% of articles come from authors belonging to the People’s Republic of China (1183 items representing 32.138%) and the USA (a percentage close to that of China with 1160 items representing 31.513% of the total). A likely reason attributed to the productivity of these countries is that these countries have several strong funding programs dedicated to soft robotics that are supported by their governments, such as DARPA ChemBots in the US or Tri-Co Robot in China; however, the main reason resides in the fact that both the USA and China have a large demographic involved in research, which allows them to publish a large number of papers in all fields, especially in new and emerging ones. The rest of the top countries each contribute less than 8%, and these countries are largely in either Europe or Asia. European countries such as England, Italy, Germany, and Switzerland account for 23.554% of articles, i.e., 867 articles, and Asia contributed 49.306% of articles, i.e., 1815 items.

Top 10 countries that have published in the field of soft robotics.

No.CountryNumber of Publications% of 3681
1People’s Republic of China118332.138%
2USA116031.513%
3South Korea2727.389%
4England2697.308%
5Italy2406.520%
6Japan2135.786%
7Germany2045.542%
8Australia1604.347%
9Switzerland1544.184%
10Singapore1473.993%

Analyzing the results according to the most productive authors in the field, Table 4 shows the top 10 authors with the highest number of articles. Majidi (USA) is the most productive author with 39 papers representing 1.059% of the total. Close behind in 2nd, 3rd, and 4th place are the Italian authors Cianchetti, Laschi, and Mazzolai with 38, 38, and 35 articles, respectively. In 5th and 6th place are two authors from China with 34 and 32 articles, followed in 7th and 8th place by two authors from the USA with 31 and 29 articles.

Top 10 authors with the highest number of articles in the field of soft robotics.

No.AuthorCountryNumber of Publications% of 3681
1MajidiUSA391.059%
2CianchettiItaly381.032%
3LaschiItaly381.032%
4MazzolaiItaly350.951%
5LiuPeople’s Republic of China340.924%
6WangPeople’s Republic of China320.869%
7WoodUSA310.842%
8WangUSA290.788%
9RossiterEngland280.761%
10DickeyUSA270.733%

Table 5 identifies the most cited articles in the WOS database for the 2008–2022 period. Table 5 also identifies the journal in which the article was published, the year of publication, the author, the country, the title of the article, and, of course, the number of citations in WOS. The most cited article in WOS is by Rus et al., with a citation count of 2596. This article was published in 2015 in the journal “ Nature ” with the title “Design, fabrication, and control of soft robots”; this is a review article providing an overview of the field of soft robotics [ 3 ]. Since its publication, this article has had a strong impact on the scientific community in the field, recording the highest increase in citations reported in a year [ 1 ]. In second place with 1641 citations is the review by Amjadi et al. titled “Stretchable, Skin-Mountable, and Wearable Strain Sensors and Their Potential Applications: A Review” [ 9 ], which was published in 2016 in “ Advanced Functional Materials” . Another review article is ranked third with 1268 citations and was written by Shepherd et al. The article is titled “Multigait soft robot” and was published in “ Proceedings of the National Academy of Sciences of the United States of America” in 2011 [ 10 ].

Top 10 most cited articles in the field from 2008 to 2022 on WOS.

No.AuthorTitleCountryJournalYearCitations (WOS)
1Rus et al.Design, fabrication, and control of soft robotsUSA“ ”20152596
2Amjadi et al.Stretchable, skin-mountable, and wearable strain sensors and their potential applications: a reviewSwitzerland“ “20161641
3Shepherd et al.Multigait soft robotUSA“ “ 20111268
4Kim et al.Soft robotics: a bioinspired evolution in roboticsUSA“ “20131109
5Ilievski et al.Soft robotics for chemistsUSA“ “20111096
6Wang et al.Skin electronics from the scalable fabrication of an intrinsically stretchable transistor arrayUSA“ ”20181033
7Tee et al.An electrically and mechanically self-healing composite with pressure- and flexion-sensitive properties for electronic skin applicationsSingapore“ “ 20121032
8Dickey et al.Stretchable and soft electronics using liquid metalsUSA“ “2017792
9Kim et al.Printing ferromagnetic domains for untethered fast-transforming soft materialsUSA“ ”2018790
10Mosadegh et al.Pneumatic networks for soft robotics that actuate rapidlyUSA“ “2014767

The 4th, 5th, and 6th place articles are occupied by three US authors who have over 1000 citations each, namely 1109, 1096, and 1033. These articles were published in the years 2013, 2011, and 2018. The 4th ranked article is a review and is titled “Soft robotics: a bioinspired evolution in robotics” [ 11 ], which was published in the journal “ Trends in Biotechnology ”. In fifth place is the article published in the journal “ Angewandte Chemie-International Edition ” titled “Soft Robotics for Chemists.” [ 12 ], and in sixth place is the article “Skin electronics from the scalable fabrication of an intrinsically stretchable transistor array” [ 13 ], which was published in the journal “ Nature ”. Tee et al. is another group of Singaporean authors with over 1000 citations, more precisely 1032. Their article was published in 2012 in the journal “ Nature Nanotechnology ” and occupies 7th position; the article is titled “An electrically and mechanically self-healing composite with pressure- and flexion-sensitive properties for electronic skin applications” [ 14 ]. The remaining positions (8, 9, and 10) are occupied by three authors from the USA who have less than a thousand citations, namely 792, 790, and 767. Their articles were published in journals dedicated to materials and one of them was published in the journal “ Nature ”. The three articles are “Stretchable and Soft Electronics using Liquid Metals” [ 15 ], “Printing ferromagnetic domains for untethered fast-transforming soft materials” [ 16 ], and “Pneumatic Networks for Soft Robotics that Actuate Rapidly” [ 17 ].

3. State of the Art in Soft Robotics

This chapter is part of the second section of this work that represents the qualitative component, which attempts to create a global but comprehensive picture of the field of soft robotics. As mentioned in chapter 2 of the bibliometric analysis of this paper, the accelerated growth and large number of articles found in the literature in the field achieves this rather challenging goal. Given the current context, a clear and objective methodology for the selection of bibliographical references is required to identify and select relevant bibliographical references. In addition to the attention paid to the methodology of reference selection, analysis of the selected bibliographic references was paid due attention to as well, with each part of the paper being analyzed in detail so that a variety of characteristics specific to soft robots could be documented in tabular form.

3.1. Methodology for the Selection of Bibliographical References

In our approach to the selection of bibliographic references, four international databases and one journal in the field were chosen. The four databases were chosen with the intention of providing greater diversity within identified fields and applications, which was achieved by choosing databases with a multidisciplinary character (WOS and ScienceDirect) and databases that offer strong technical features (IEEEXplore and SpringerLink). The “ Soft Robotics ” journal was chosen since it only publishes articles in the field of soft robotics. All these databases were selected to increase the relevance of the study as well as to satisfy its multidisciplinary character.

This study was based on research articles and reviews written in English during the 2018–July 2022 timeframe. This range, according to the bibliometric analysis above, represents 87.63% of all research and review articles identified from the four databases and the journal. This confirms that the relevance of this study is significant. The exact search term chosen to identify relevant bibliographic references was “Soft Robotics”. This expression best characterizes the domain of the same name. In the database search field, the exact phrase was entered using quotation marks, and all results were sorted by their relevance while applying the criteria mentioned below.

The search identified an impressive number of research articles and reviews, with 6400 results identified across the four databases and the “ Soft Robotics ” journal. Due to a large number of papers found, it was decided that the selection of articles would be carried out in two stages based on clear criteria. A graph of the search process is shown in Figure 2 (inclusion criteria). For the first selection stage, the eligibility criteria on which the selection of articles was based were related to the following:

  • Specific characteristics of soft robots are identified;
  • Materials and actuators are used that provide compliance to soft robots;
  • Manufacturing methods, sensors, and domain-specific modeling methods are identified;
  • The article clearly and concisely presents data on the structure of the article.

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Graph of the selection process of the bibliographic references relevant to this analysis according to [ 18 ].

A total of 5576 articles were excluded in the first selection phase by following the eligibility criteria mentioned above. Analysis of the articles for selection was mainly based on a careful analysis of the abstracts of the articles and, to further increase the relevance of the study, a visual scan of the entire article was also performed. A significant number of duplicate articles were excluded from the analysis as they were found in several databases. Firstly, duplicate articles found in multiple databases were removed. Secondly, some articles were removed because the full article was not available, and most articles were removed because they did not deal specifically with the field of soft robotics. After the first selection stage, a total of 824 articles were obtained, which were analyzed in the second selection stage.

A large number of publications was taken from the “ Soft Robotics ” journal. Additionally, a considerable number of publications were retrieved from the WOS and ScienceDirect databases, as these being databases contain an impressive number of publications.

In the second selection stage, 111 articles were selected from the 824 publications for state-of-the-art analysis. In this stage, the selection of articles was conducted according to detailed analysis of the whole article, and the selection was based on the following exclusion criteria:

  • The work reviewed should clearly and sufficiently present the issues addressed;
  • Diversity in soft robot applications;
  • The variety of aspects related to materials, actuators, manufacturing technologies, sensors, and control systems used in the current soft robot framework;
  • Aspects related to the mode and source of energy used in the operation of soft robots;
  • Validation of the performance of soft robots through various numerical, experimental, or analytical analysis methods.

At this stage, 713 articles were excluded, with the majority of articles being excluded due to the following issues:

  • Works dealing with similar issues;
  • Insufficient or unclear explanations related to the implementation method;
  • Insufficient data related to the methods used;
  • The paper does not use sufficient methods of analysis and validation;
  • The work is not part of the specifics of the field.

3.2. Analysis of Bibliographical References

Analysis of the bibliographical references was performed from the perspective of three different directions. We thus proposed the analysis of the selected publications from a perspective related to the design principles of soft robots (biologically inspired soft robotics), from the perspective of functionality (closed- or open-loop control), and from the perspective of applications (applications of soft robots in the biomedical field). With this approach we tried to capture new and valuable aspects compared to other review articles. We also approached the analysis of bibliographic references according to the components of soft robots that are presented in the tables in the appendix of the paper ( Table A2 , Analysis of bibliographic references according to the materials; Table A3 , Analysis of bibliographic references according to the actuators; Table A4 , Analysis of references according the specific technologies; and Table A5 , Analysis of references according to the modelling methods; Table A6 , Analysis of bibliographic references according to the sensors).

3.2.1. Bio-Inspired Soft Robots

Biological organisms such as animals rely on the deformation of their body structure during locomotion. Their implicitly compliant deformable structure gives them efficient locomotion in the natural environments in which they live. These characteristics of living things have inspired engineers and researchers to integrate nature-inspired elements into their robotic structures, equipping robots with the ability to interact adaptively to unpredictable and unknown environments. Coyle et al. presented biologically inspired soft robots from a mechanical perspective, specifically related to design, material choice, and actuation [ 19 ]. Ren et al. compared the capabilities of soft robots to those of biological systems. According to them, there is still a large discrepancy between the two in terms of autonomy and integrated structures such that biologically inspired soft robots can only achieve “natural life artificially”. Some of these gaps are related to materials, control, and data processing algorithms, with flexible sensors and finite element simulation methods just some of the components of soft robots where significant developments are needed to realize bio-integrated and autonomous soft robots [ 20 ]. Mahdi et al. discussed publications from 2017 to 2020 from the perspective of the materials used in the realization of soft actuators and sensors. As for soft actuators, they have developed in terms of actuation parts and mechanical properties being improved; however, they are still yet to be integrated into industrial or commercial applications and improvements are still needed in terms of output force and limited lifetime. Regarding soft sensors, their accuracy, sensing range, and sensor linearity issues, they require additional analysis and modeling [ 21 ].

Liu et al. proposed a miniaturized bio-inspired robot with grasping capabilities and crawling and jumping locomotion capabilities in wet environments that can be used in medical applications such as drug delivery. The robot is based on a structure that has five layers, with each layer being 20 μm thick and possessing different functionalities when assembled. These layers include the pneumatically actuated actuator, as well as a layer with sensing properties that provides the possibility of closed-loop control [ 22 ]. Qin et al. also developed a crawling locomotion robot based on the use of springs and electrostatic actuators for legs that was vacuum-driven with fast locomotion and movement on vertical surfaces [ 23 ]. Guo et al. developed a soft robot with crawling realized through locomotion based on two EA legs, and the robot also had a dielectric elastomeric actuator inside that was a pre-tensioned spring that could help the robot during locomotion [ 24 ]. Another type was a bio-inspired robot with crawling locomotion that was driven by magnetic fields and which had PrFeB microparticles in the structure; this type of robot was made by V. K. Venkiteswaran et al. [ 25 ]. Niu et al. proposed a magnetically actuated crawling through locomotion robot that is not connected to an external component. The robot is driven by a rotating platform with permanent magnets that move constantly, namely by driving the robot in the direction of platform movement [ 26 ]. Zhang et al. proposed a soft robot inspired by the propulsion system of cuttlefish (cephalopods). It is based on a biomimetic siphon equipped with a diameter-varying pressure control channel, which represents the propulsion system, and the corresponding omnidirectional motion of orientation is achieved using three siphons positioned on the circumference of the propulsion siphon [ 27 ]. The issue of improving the lives of people with disabilities was addressed by Feng et al., who developed an artificial hand based on fluid actuators reinforced with fiber that contained three independently actuated cavities. This artificial hand was controlled by pressurization as well as by the capture of myoelectric hand signals by surface electrodes. The artificial hand’s control system is based on two control components, one corresponding to finger actuation by solenoid valves and pressure sensors and one corresponding to the human–computer interface seen in Figure 3 (a) [ 28 ]. Caterpillar locomotion was a source of inspiration for Zou et al., who developed a reconfigurable modular soft robot with omnidirectional locomotion composed of nine independent pneumatically actuated modules that was controlled via solenoid valves and pressure sensors that set the robot in motion according to the desired configuration [ 29 ]. Sui et al. simulated the behavior of a modular robot in VoxCAD software to validate the model and reduce design time, as shown in Figure 3 (b) [ 30 ]. Caterpillar locomotion also inspired Li et al., who developed a soft unconnected robot with a dielectric elastomer-based drive that moves at a speed of 100 mm/s [ 31 ]. Li et al. also developed a series of robots with actuators based on dielectric elastomers that can move at a speed of 0.65 m/s with a diameter of 106 mm [ 32 ]. Jung-Hwan et al. in their review discussed the applications of soft-actuated robots based on dielectric elastomer actuators (DEA). In this category of actuators, the authors identified a couple of challenges that have limit their development, such as increased voltage levels for actuating the actuators (which is undesirable for wearable applications), the increased amplitude of motion, and power output [ 33 ].

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( a ) Finger actuator structure; reproduced with permission from [ 28 ]; published by ELSEVIER, 2019; ( b ) modular robot simulated in VoxCAD software [ 30 ].

Another soft robot with crawling locomotion was designed by Mc Caffrey et al. and is driven by shape memory alloys (SMAs) [ 34 ]. Li et al. developed an eight-spring-driven circular robot with SMAs and flexible sensors with closed-loop control [ 35 ]. Another case is represented by a pipeline exploration robot based on a crawling locomotion soft robot, which is actuated by three fluidic actuators with open-loop control; this was designed by Zhang et al. [ 36 ]. Zhou et al. proposed a gripper based on fluid actuators that have granules in the structure to provide passive variable stiffness during body–finger contact [ 37 ]. Calderón et al. proposed a type of robot inspired by earthworm locomotion that is based on two radial and one axial pneumatic actuator and an artificial skin sensor. The control is based on an Arduino Mega microcontroller on which the control strategy of the pneumatic components and sensors of the robot is based [ 38 ]. Gu et al. proposed a fluid actuator whose chambers are inclined at a given angle across the actuator surface and, based on this configuration, the actuator was capable of combined bending and twisting motions [ 39 ]. Instead, Hu et al. developed two actuator configurations, one with tilted cameras 3D-printed on the whole actuator surface and one with a hybrid actuator with tilted and non-tilted cameras that can be configured according to the specific application [ 40 ]. Jizhuang et al. developed a soft robot based on frog locomotion that is driven by fluid actuators, and the robot is capable of linear displacements and rotations [ 41 ]. Tang et al. were inspired by the kinematics of cheetahs’ spines during galloping and created a bio-inspired robot based on this principle. The robot is driven by fluid actuators that are connected through hoses to an air supply and has an open-loop control system [ 42 ]. Coral W et al. developed a fish-like robot driven using shape memory alloys (SMA) that is equipped with bending and current sensors to help control the robot [ 43 ]. Berg et al. made an open-source cable-driven fish from a DC motor with a gear mechanism [ 44 ].

Shintake et al. developed a fish-like robot with dielectric elastomer actuators [ 45 ]. Deng et al. developed a robotic table that can manipulate various objects in the xoy plane by deforming the contact surface. The deformable table is composed of 25 individual pneumatically actuated modules controlled via solenoid valves and an Arduino microcontroller [ 46 ]. Chen et al. developed a cube-shaped soft robot that performs locomotion by rolling where the driving is based on an inertial measurement unit (IMU) that identifies the surface that is in contact with the ground; the actuation is performed by fluid actuators [ 47 ]. The locomotion of quadrupeds inspired Li et al. to make an autonomous four-legged robot that is not connected to an external power source, thus giving it an increased workspace. The legs are based on a hybrid drive composed of fluidic actuators and nylon cable-based actuators, as well as servo motors [ 48 ]. Referring to the manufacturing technologies used in the field of soft robotics, Schmitt et al. discussed the state of the art in the field of soft robot manufacturing methods. From the diverse applications they reviewed, the manufacturing methods most often identified were molding manufacturing methods involving injection molds and additive manufacturing (also called 3D printing) [ 49 ]. Additive manufacturing technology applied in the manufacture of soft robots was reviewed in detail by Stano et al., who found three approaches to the use of additive manufacturing in the field of soft robots. These three approaches are related to the realization of injection molds by 3D printing processes, hybrid 3D manufacturing, and full additive 3D manufacturing (modular and monolithic). They also found that the use of 3D printing needs to move from a passive approach involving only the making of molds or other related components to a hybrid or fully additive approach in which soft robotic structures are entirely made by the 3D manufacturing process [ 50 ]. Gul et al. in their review analyzed the main challenges of using 3D printing technologies to make soft robots. These challenges are related to the fabrication of fully 3D printed soft robots, limited soft materials, challenges related to printing with multiple materials, and issues related to adhesion between materials [ 51 ]. Hann et al. discussed 4D printing in soft robots in their review. They identified certain approaches related to the choice of shape memory material (SMM), more specifically shape memory polymers (SMP), and the diversification of the range of materials with shape memory properties for as many reversible actuations as possible [ 52 ].

3.2.2. Aspects Concerning the Open-Loop and Closed-Loop Control of Soft Robots

In the paper by Liu et al., the robot driving system was based on closed-loop robot driving. Data from the EGaIn sensor mounted on the robot is collected by the Arduino UNO development board, which drives a servo motor via a PWM signal, driving the 1 mL syringes that supply air to the robot for locomotion [ 22 ]. Zhang et al. used both control variants (closed-loop, open-loop). A closed-loop was used for adjusting the water drive system of the propulsion system, as well as the orientation actuators, and robot control was performed in an open loop as there was an IMU sensor mounted on the manipulator end used for its calibration [ 27 ]. Feng et al. also approached the control of robotic hands through two control components: one with precise control of pressure and flow that pressurizes the fingers and one with control based on the human–computer interface (realized in Labview software). An Arduino UNO development board was used as the information processing unit to control the process of manipulating objects for people with upper limb disabilities, as shown in Figure 4 a [ 28 ]. Jaryani et al. approached a similar method of control but, due to the specificity of the application, they also used vacuum actuation to meet the rehabilitation needs of the patients ( Figure 4 b) [ 53 ]. Sun et al. approached the control of autonomous prehension from the perspective of three levels of control: actuation, information processing, and user interface. The use of sensors makes the prehensor possess some level of autonomy, but the prehensor control is limited due to comparison with the existing database that validates the action depending on the object visible ( Figure 4 c) [ 54 ]. Gong Z. et al.’s approach to the manipulator and prehensor kinematic control method for collection activities in aquatic environments was based on inverse kinematics with closed-loop control for two-dimensional and three-dimensional trajectory tracking using video cameras, as shown in Figure 4 d [ 55 ]. A similar approach with a dynamic manipulator control was proposed by Thuruthel et al. [ 56 ]. Xing Z. et al. proposed a manipulator with five modules made of PET and flexible plastic driven by dielectric elastomers. The control is an open-loop type of control that is effectuated by a custom controller consisting mostly of a PLC and high-voltage relays [ 57 ]. Yang et al. developed a pneumatically actuated manipulator through pressurization and the use of a vacuum that used joints based on rotary actuators; the manipulator employed closed-loop control with a positioning accuracy of less than 1 cm [ 58 ].

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( a ) Control using vacuum actuation; reproduced with permission [ 28 ]; published by ELSEVIER, 2019.; ( b ) diagram of a fluid actuator hand control scheme; reproduced with permission from [ 53 ]; published by ELSEVIER, 2020; ( c ) prehensor control based on a fluid actuator with scales inspired by pangolin skin structure; reproduced with permission from [ 54 ]; published by ELSEVIER, 2020; ( d ) control scheme of a manipulator with joints based on rotating fluidic actuators [ 55 ].

Nguyen et al. developed a pneumatically operated manipulator with a built-in gripper for handling tasks with various objects. The manipulator is positioned on the person’s body, representing an upper third limb. It is controlled by the user via a joystick and is equipped with EMG sensors to capture muscle intention [ 59 ]. Cheng et al. proposed a manipulator based on SMA actuators that has nine degrees of freedom and closed-loop control that employs gyroscope and accelerometer modules [ 60 ] or manipulators driven by SMA coils and Hall sensors [ 61 ]. Li et al. proposed an SMA-driven manipulator position control method based on fuzzy delay algorithms to increase manipulator accuracy due to the nonlinear hysteretic behavior of SMAs [ 62 ]. Jizhuang et al. approached the control of the frog robot through an open-loop control system that connected an HC-12 module to the robot microcontroller, which allowed the robot to be controlled from a PC. The drive system is specific to pneumatic actuators and the robot has high autonomy while not being tied to an external power source [ 41 ].

3.2.3. Soft Robots with Applications in Medicine

Highly compliant materials in the structure of soft robots offer great potential for the development of medical equipment and devices due to their mechanical simplicity and a high degree of similarity to the structures and tissue of living organisms. Jen-Hsuan et al. in their review discussed recent achievements in the field of soft robot applications in the medical field. For minimally invasive surgery applications, soft robotics accelerated the development in this field through intrinsic properties, and for rehabilitation and assistive devices, soft robotics greatly improved biocompatibility. In the medical field, soft robotics offers another approach based on safety and efficiency in human–device interaction [ 63 ]. Yarali et al. in their review discussed the potential of soft robots made of magneto/electro-responsive polymers (MERPs) in medical engineering, such as their use in drug delivery applications in the human body or artificial tissues. The use of MERPs in biomedical engineering has great potential for development, but to determine the behavior of MERPs in in-vitro environments additional studies are needed [ 64 ]. Additionally, Eshaghi et al. confirmed in their review of soft magnetic robot applications that these are still in their infancy and offer great potential in biomedical and non-biomedical applications; however, further studies in both in-vivo and in-vitro environments are needed [ 65 ]. According to Hyegyo et al., in the field of hybrid soft robots with nanomaterial, 2DLMs (two-dimensional layered materials) or liquid crystals that have responsive behavior to external stimuli are limited in terms of their integration into real applications. The most advanced soft robots in this field are “stuck” in a conceptual state due to nonlinearity, response time, and prediction of shape deformation under certain stimuli, these being just some of the challenges faced by this field [ 66 ]. Another material that is being used more and more due to its properties, and which is still in its infancy, is hydrogel-based soft robots. This material has high elasticity, transparency, ionic conductivity, and biocompatibility; however, these soft robots need new approaches if they are to be integrated into real applications [ 67 ]. A new series of liquid metal (gallium)-based soft robots has been developed that possesses flexible sensors and actuators for biomedical and non-medical applications. These materials are increasingly used due to their good electroconductivity and high elasticity [ 68 ]. Graphene is also another material with promising characteristics for soft robotics, especially in making sensors and actuators with improved sensitivity and selectivity. Limitations in this field are related to the high-quality production of graphene, compatibility with other materials, and the use of graphene-based soft robots in industrial environments [ 69 ]. Textiles integrated into soft robotics have had a significant increase in application and improved technical characteristics; however, the efficiency and characteristics of soft robots with textile structures in practical applications are limiting [ 70 ].

Lindenroth et al. proposed a medical robot for treating ear diseases that is designed to identify and inject medication precisely without unwanted movements that cause pain to the patient. This is achieved by locomotion within the ear canal utilizing six fluidic actuators that, through combined actuations, perform positioning and orientation movements. So as to detect the optimal injection area, a detection system was developed using a miniature camera, as shown in Figure 5 a [ 71 ]. Jaryani et al. developed a glove-like exoskeleton for hand rehabilitation using fluid actuators with semi-rigid segments resembling the structure of human fingers. Each finger is actuated by individual pressurization and vacuum through proportional solenoid valves. In addition to pressure and the vacuum sensors, IMU sensors mounted on the fingertips were used to provide feedback to the control system ( Figure 5 b) [ 53 ]. Heung et al. proposed a wearable hand rehabilitation glove for people with stroke. The glove consists of five pneumatically actuated fiber-reinforced fingers. Its control is based on solenoid valves that pressurize or depressurize fluid actuators [ 72 ]. Bützer et al. and Burns et al. also developed an exoskeleton for hand rehabilitation that is operated by cables only, which is intended for people who have suffered a stroke or spinal cord injury (SCI) [ 73 , 74 ]. In colorectal cancer, McCandless et al. proposed a soft robotic sleeve to increase navigation safety during the colonoscopy process. The robot attaches to the endoscopic device and provides feedback via optical sensors. Additionally, at a certain value set by the physician via the GUI (Graphical User Interface) in Matlab, the robot will pressurize the three circularly arranged actuators to redistribute pressure over a larger area during navigation [ 75 ].

Hip flexion rehabilitation was investigated by Miller et al., who proposed a robotic device based on rotating fluid actuators that is controlled by myoelectric signal capture and IMU sensors ( Figure 5 c) [ 76 ]. In the paper by Joyee et al., a soft robot with multimodal caterpillar-like locomotion is realized, which operates unconnected to an external power source. The robot was 3D printed by a special magnetic field stereolithography process (M-PSL) and was designed to deliver drugs into living organisms, as shown in Figure 5 (d) [ 77 ]. Controlled using EMG signal capture, Nam et al. developed a device composed of two elements designed for elbow and hand joint rehabilitation ( Figure 5 e) [ 78 ]. Lindenroth et al. proposed a robot for ultrasound medical imaging based on fluid actuators that provide safe interaction between the device and the patient. Position control is performed in a closed loop based on an electromagnetic tracking sensor and a six-axis NANO 17 force/torque sensor, all guided by a joystick by the physician [ 79 ]. Thai et al. proposed a flexible soft robot with applications in surgical medicine. It has a simple configuration as it is driven based on a soft microtube artificial muscle (SMAM) actuator composed of a flexible silicon microtube and a coil [ 80 ]. Saeed et al. proposed an implantable ventricular assist robot to increase left ventricular contractions. It uses a McKibben artificial muscle-type pneumatic actuator, as shown in Figure 5 f [ 81 ]. Considering esophageal cancer, Bhattacharya et al. proposed an endoprosthetic stent-like soft rehabilitation robot for people suffering from dysphagia due to the mentioned disease. The stent is based on a 12-layer fluid actuator, with each layer having four chambers arranged circularly. When pressurized, the chambers expand and block the cross-section of the food passage. The control system is based on the use of 12 proportional valves that pressurize each layer of the stent [ 82 ]. Dang et al. developed a biological-like gastric simulator based on simulated gastric peristaltic contractions and the principles of soft robotics. The contractions are performed by pneumatic actuators and the manometry process was used to monitor contractile force [ 83 ].

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( a ) Medical robot designed to treat ear diseases [ 71 ]; ( b ) rehabilitation glove; reproduced with permission from [ 53 ]; published by ELSEVIER, 2020; ( c ) robotic device for hip joint rehabilitation using rotating fluid actuators [ 76 ]; ( d ) multimodal locomotion robot for drug delivery; reproduced with permission from [ 77 ]; published by ELSEVIER, 2020.; ( e ) wearable device for upper limb recovery after stroke [ 78 ]; ( f ) ventricular assist device with McKibben actuator [ 81 ].

4. Conclusions and Future Directions

In this paper, the field of soft robotics has been analyzed from both quantitative and qualitative perspectives. The quantitative analysis was based on a bibliometric analysis of the field of soft robotics concerning its evolution in the 2008–2022 period. Four databases (WOS, ScienceDirect, IEEEXplore, SpringerLink) and a specialized journal titled “ Soft Robotics ” were searched, resulting in a total number of 7646 articles. From the graph analyzing the evolution of the field ( Figure 1 ), the number of articles has increased considerably since 2018. This is based on the intensification of research in the field due to the rapid evolution of related fields, such as 3D printing and materials engineering. Additionally, this increase is also the result of the identification of new applications for soft robots. We believe that future trends will continue until the field reaches full maturity and then saturation. The bibliometric analysis was carried out on the WOS database, specifically the Core Collection. Only research and review articles were included in the analysis of the 2008–July 2022 period, thus the number of publications included in the analysis was 3681. In this analysis, numerous characteristics related to the WOS domains that contributed most to the field, namely authors, countries, productive journals, and most cited articles on WOS, were analyzed in terms of the number of publications. The analysis shows that the field of “Materials Science Multidisciplinary” contributed the most publications, followed by the field of “Robotics”. The most productive journal was “ Soft Robotics ” with more than 450 articles. In terms of countries and productive authors in the field, China and the USA were at the top with a close number of articles, and their productive authors also contributed more than 1% of the total number of publications. The article by Rus et al. [ 3 ] had the highest number of citations with more than 2500 citations on WOS.

The qualitative analysis was the second component addressed in this paper and was based on a total of 111 research and review articles in the 2018–July 2022 timeframe. The articles were identified from four international databases and a peer-reviewed journal based on the search phrase “Soft Robotics”, which resulted in a total of 6400 articles. Due to the large number of articles identified, the selection of articles was conducted in two stages to increase the relevance of this study. The selection of articles was based on a set of clear criteria for inclusion in each selection stage. Table A1 ( Appendix A ) provides a general analysis of the bibliographic references, specifying the field of application, the materials, the manufacturing technologies, and the main elements in the structure (actuators, sensors). Analysis of the 111 articles was treated from the perspective of three areas of interest: design (biologically inspired soft robots), functionality (open-loop and closed-loop control of soft robots), and applications (soft robots with applications in medicine). The 111 selected bibliographical references have also been analyzed in tabular form according to the materials ( Table A2 ), actuators ( Table A3 ), manufacturing technologies ( Table A4 ), modeling methods ( Table A5 ), and sensors ( Table A5 ) used ( Appendix A ). As a result of the analysis, some conclusions have been identified regarding the main issues specific to soft robots, and the limitations of each technology and future directions in this area are highlighted below.

It is a certainty that the field of soft robotics is in continuous development given the number of publications and previous reviews, including the present one. According to the present review, the field of soft actuators has developed considerably, especially their operation and properties, and there is a wide range of actuation methods. The most common actuators encountered in the analysis were fluidic actuators of various types, configurations, and reinforcements, which were most often actuated by pressurization and less often by vacuum (or both simultaneously). Use of a specific type of actuator was determined by the specific application. Other common actuation methods included electrically actuated actuators, such as dielectric elastomers (DEA), and shape memory alloy (SMA)-based actuators. Each of these actuation methods has advantages and disadvantages and the choice of an actuator variant requires identification of the optimal characteristics concerning the specific application. The problems found in the analysis are still related to limited force output and limited lifetime.

Concerning the sensors currently used in soft robotics, sensors with a direct role in capturing information from the soft robot by being integrated into the robot’s structure and deforming with the robot structure are predominantly used. These are specifically liquid metal-based sensors (EGaIn) and flexible bending sensors. Regarding sensors with an indirect role (those capturing data from the experimental setup of the robot), pressure, force, current, voltage, laser, ultrasonic, and video camera sensors are most often found. Direct role sensors (the flexible ones) do not offer many options for applications and face various limitations in terms of accuracy, sensing range, and sensor linearity.

Concerning the manufacturing methods of soft robots, the methods most often identified in this review and other similar works are molding methods that use molds and 3D printing. Casting technology offers advantages in terms of part complexity; however, manufacturing time is longer. In the case of 3D printing, future research directions identified in the literature are related to the transition from the 3D printing of molds to full 3D printing of soft robots; however, this requires the realization of new soft materials, simultaneous printing with different materials, and solutions to issues related to their behavior and adhesion. Steps have been made towards full 3D printing with soft materials and 3D printing processes that realize soft structures, such as soft lithography or magnetic field stereolithography (M-PSL), these being some of the new manufacturing technologies identified that may offer new opportunities for the realization of soft robots.

From the perspective of materials used in soft robots, there is a considerable variety available. In the present analysis, most of the materials used were elastomer-based materials, and in this category we identified Ecoflex and DragonSkin bi-component silicone materials from Smooth-On being used in the molding process. Common materials identified in the analysis of 3D printing included acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA), which were used for making the molds and various semi-rigid components of the robotic structure. The analysis identified certain materials that react to various stimuli that have high potential in terms of the manufacture of medical or non-medical equipment and devices, such as drug delivery, surgery, and rehabilitation devices. These materials also have potential for assistive applications as they are similar to the structures and tissues of living organisms. These materials, such as magneto/electro-responsive polymers (MERPs), hybrid robots with 2DLMs (two-dimensional layered materials) or liquid crystals, hydrogel-based robots, liquid metal (gallium)-based robots, and graphene- or textile-based robots, have great potential in the medical and non-medical field but have several limitations, which has led to them being seen as “stuck” in the testing stages. Magneto/electro-responsive polymers have great potential in drug delivery but, to move beyond the test approach and into real-world applications, additional testing and analysis 3in in-vivo and in-vitro environments is required to accurately determine their behavior in the presence of stimuli [ 64 , 65 ]. Additionally, hybrid robots with 2DLMs (two-dimensional layered materials), nanomaterials, or liquid crystals represent another type of materials that respond to stimuli; however, they are limited in their applications due to being locked into limitations related to nonlinearities, response times, and the prediction of shape deformation under certain stimuli [ 66 ]. Another category is represented by graphene-based robots, a material that is increasingly used due to its properties. This material is present in the realization of sensors and soft actuators, making a substantial contribution to improvements in their sensitivity and selectivity [ 69 ].

There are manifold directions in soft robotics that mainly aim to increase the autonomy and integrability of soft robots so as to achieve the performance of biological organisms, thus exhibiting “natural life artificially” [ 20 ]. The key components in achieving this goal are related to control (control algorithms and data processing), flexible sensors, and connecting or tethering the robot by cables or hoses to an external power source, which greatly limits its autonomy and behavior. Analyzing the control component of soft robots, the approaches found in the reviewed publications address both closed-loop and open-loop control in similar proportion, while there are also hybrid approaches that combine the two variants. Concerning closed-loop control, the analysis identified different approaches to controlling soft robots precisely and autonomously. One approach was the use of flexible or bending sensors mounted or integrated into the structure that collected data once the structure had deformed, thereby closing the feedback loop. This approach is somewhat limiting because, as more flexible sensors are integrated to determine motion variations, the difficulty of the control component increases significantly. Another closed-loop control approach identified in the analysis was based on a control algorithm that used image processing, which was realized by integrating video cameras that continuously monitored the deformability state of the robot as a function of the objects it interacted with. Additionally, in the case of soft manipulators where control is an important challenge, control approaches are more focused on kinematic control based on quantitative and qualitative kinematic methods and less on approximate behavioral control methods based on dynamic models that also take into account the influence of forces acting on the manipulator during operation.

Due to the non-linear behavior of elastic materials in the soft robot structure, the modeling methods most often used and identified in the analysis are numerical and experimental modeling methods, while analytical methods are less frequently used. The numerical finite element modeling programs most often used in the analysis were Abaqus (Dassault Systèmes) and Ansys, which offer the possibility of simulating and visualizing the results of analysis. There are also other approaches identified depending on the specifics of the applications, for example, in the case of modular reconfigurable robots, there is a need for a 3D simulation and visualization platform of the behavior of the modules that can shorten design time, reduce costs, and verify the effectiveness of algorithms.

Based on the present analysis, some future research directions have been identified to improve the future characteristics of soft robots so that they may reach characteristics comparable to those of biological beings while also being feasible in industry or commercially available devices. These directions relate to autonomy, integrability, material capabilities to withstand various environmental stresses, controllability, flexible sensors, actuation methods, and manufacturing methods adapted to soft robots. The first area where further research is needed is related to the autonomy of soft robots, which is currently severely limited by the connection to external power supplies as this strongly affects the robot workspace and negatively influences the behavior of the soft robot. With a focus on achieving these characteristics, there are some limitations related to the miniaturization of the components to be integrated, especially in terms of meeting the dimensional criteria corresponding to biological organisms.

Another direction that implicitly also leads to increased autonomy and requires new approaches in research is related to the closed-loop control or feedback control of soft robots. The use of feedback in the control of soft robots is based on the use of flexible sensors within the external structure of the soft robot that transmit data related to the position and deformation of the robot structure. A limiting factor in the use of closed-loop control is closely related to the flexible sensors used, which offer a limited range of available options and also have important limitations. Another limitation that can hamper control is related to the use of a large number of flexible sensors for the satisfaction of control requirements, thus transmitting a multitude of data that makes it difficult to implement the control algorithm.

Another future research direction is related to the development and improvement of 3D additive manufacturing processes that offer the possibility of making soft robots entirely out of more soft materials, as well as the possibility of making soft robots with integrated internal structures such as sensors. One possible way to realize these robots is through 3D printing methods such as soft lithography or magnetic field stereolithography (M-PSL). To achieve the performance of biological beings in terms of autonomy, integrability, adaptability, and efficient locomotion, soft robots still have many aspects that need to be improved or developed in order to achieve these goals, especially if they are to be used in industrial or commercial applications. These limitations and challenges have been identified and addressed above, while this entire paper has aimed to create an overview of the evolution and current state of research in the field of soft robotics while at the same time highlighting research directions in the field.

General analysis of bibliographic references.

AuthorRef.Field of ApplicationManufacturing TechnologiesThe Material UsedActuatorSensor
Liu et al.[ ]LocomotionSoft lithography, laser processingSMP, CuNi, Ecoflex 20, Silgard184 with silk threads and particlesFluidic actuator—airEGain
Zhang et al.[ ]LocomotionCastingDragon Skin 10, 30, Ecoflex -30Fluidic actuator—waterPressure, flow, IMU
Feng et al. [ ]ManipulationCastingEcoflex 00–50, Dragon Skin 30Fluidic actuator—airPressure sensor, bending, micro dynamometer, EMG
Lindenroth et al. [ ]Medical devicesCastingEcoflex 00-30, 00-50, Dragon Skin Fx Pro, Smooth-Sil 960Fluidic actuator—deionized waterModule camera MD-V1001L-91X
Jaryani et al. [ ]Medical devices, rehabilitationCastingSilicone rubber (XIAMETER RTV-4234-T4)Fluidic actuator—airPressure–vacuum sensor, IMU
Zou et al. [ ]LocomotionCastingDragon Skin 30, Ecoflex 00-30Fluidic actuator—airPressure sensor, dynamometer
Sun et al. [ ]PrehensionCasting actuator, 3D printing layer with variable stiffnessDragon Skin 30, rubber, nylonFluidic actuator—airBending, force, pressure, ultrasonic
Gong et al. [ ]Manipulation, prehensionCastingDragon Skin 10, 30Fluidic actuator—airStereo camera, video camera
McCandless et al. [ ]Medical devicesCastingEcoflex 00-30, VytaflexTM 20Fluidic actuator—airSoft optical sensors, pressure
Xing et al. [ ]ManipulationCasting, laser cuttingConductive carbon grease, PET, flexible plastic, VHB_ 4910Dielectric elastomer -
Qin et al. [ ]Locomotion-Polyester fabric, thermoplastic polyurethaneElectrostatic actuator, VASALaser sensor, digital
Miller et al. [ ]Medical devices, rehabilitationHeat sealing, 3D printingNylon fabric coated with thermoplastic polyurethane (TPU)Rotary fluidic actuator—airForce, IMU, EMG
Zhou et al. [ ]PrehensionCasting, 3D printingDragon Skin 20, 30Fluidic actuator—air-
Joyee et al.[ ]Locomotion3D printing, stereolithography (M-PSL)Spot E elastic, magnetic nanoparticles—EMG 1200Electromagnetic actuator-
Li et al. [ ]LocomotionCastingEcoflex 00–30, silicon dioxide nanoparticles, acrylonitrile-butadiene styrene—ABSDielectric elastomer—DEForce sensor
Calderón et al. [ ]LocomotionCastingEcoflex 00–50, 00-30, butadiene rubber, fiberglassFluidic actuator—airLiquid metal—galinstan
Nam et al. [ ]Medical devices, rehabilitation3D printingPVC, photopolymerPneumatic Artificial Muscles (PAM)Pressure, EMG
Li et al.[ ]Prehension--Pneumatic Artificial Muscles (PAM)-
Gu et al. [ ]Drive, prehensionCastingElastosil M4601, Ecoflex 00–30.Fluidic actuator—air-
Guo et al.[ ]Locomotion-Polyimide—dielectric layer, copper layer—electrode, Sylgard 184—insulation layer, dielectric elastomer (VHB 4910), carbon grease (846-80G)Dielectric elastomer (DEA), flexible electroadhesive (EA)Video camera, laser sensor
Venkiteswaran et al. [ ]LocomotionCastingPraseodymium powder (PrFeB), silicone Ecoflex 00-10Magnetic actuator-
Hu et al. [ ]Drive, prehension3D printingFilaFlex—thermoplastic elastomerFluidic actuator—airPressure sensor, force
Sui et al. [ ]LocomotionCastingSilicone Ecoflex 00-50, radial magnetsFluidic actuator—airUltrasonic
Jizhuang et al. [ ]LocomotionCastingSilicone Ecoflex 00-50Fluidic actuator—air (Cuboid)Motion sensor, pressure
Perez-Guagnelli et al.[ ]Medical devicesCastingSilicone Ecoflex 00-30, polyesterHelicoidal fluidic actuator (SoPHIA)Distance, force
Sonar et al. [ ]ControlCastingPolydimethylsiloxane (PDSM), Sylgard 184Fluidic actuator—air (SPA)EGaIn
Caffrey et al. [ ]Locomotion3D printingTangoBlack+, VeroWhite+Shape Memory Actuator (SMA)-
Yi et al.[ ]Drive3D printingFlexible thermoplastic polyurethaneRotary fluidic actuator—airPressure sensor, torque,
Coral et al. [ ]Locomotion3D printingPolycarbonate, plastic (ABS), lycra fiber, latex, liquid silicone, silicone paintShape Memory Actuator (SMA)Bend sensor, current, temperature
Yang et al. [ ]Manipulation3D printing, bonding by heat pressingPoplin, thermoplastic polyurethane (TPU), acrylonitrile butadiene styrene (ABS)Rotary fluidic actuator IMU sensor, pressure
Seref Kemal Talas et al. [ ]Drive3D printingPolyethylene terephthalate, polylactic acid (PLA), polytetrafluoroethylene (PTFE), polyamide 12, latexFluidic actuator—airForce/torque sensor
Cheng et al. [ ]Manipulation3D printing-Shape Memory Actuator (SMA)Gyroscope sensor (MPU)
Herianto et al.[ ]Drive, prehension3D printing (FDM)Thermoplastic polyurethane elastomerFluidic actuator—air-
Li et al.[ ]DriveCastingSilicone elastomer (605, 5HA), Ni-Cr resistive firFluidic actuator—air-
Youxu et al.[ ]Locomotion3D printingSilicone rubber Ecoflex 00-50, Dow Corning 737, polylactic acid (PLA), liquid metalFluidic actuator—airEGaIn
Yang et al. [ ]Manipulation, locomotionCastingSilicone rubber, silicone gelShape Memory Actuator (SMA—Flexinol)Hall sensor
Lindenroth et al. [ ]Medical devicesCastingSilicone rubber DragonSkin 10-NV, SmoothSil 945Fluidic actuator—airForce/torque sensor, electromagnetic tracking sensor
Zhang et al. [ ]LocomotionCasting, 3D printingEcoflex 00-50, Kevlar fiber, adhesive (HJ-420)Fluidic actuator—airPressure
Ohta et al. [ ]Manipulation3D printingSilicone, carbon fiber rods, fiberglass, photopolymer, polyurethane sheetsFluidic actuator—airPotentiometer
Thai et al. [ ]Medical devices3D printingFlexible silicone microtube, micro-coilArtificial muscles with soft microtubules (SAM)-
Liu et al. [ ]Manipulation3D printing (SLS)Silicone rubber, nylonFluidic actuator—air-
Li et al. [ ]Medical devicesCastingElastosil M4601Actuator with cablesMicro video camera
Roozendaal et al.[ ]Devices for increasing comfortCastingDragonSkin 30, foam (Octaspring)Fluidic actuator—airPressure sensor, phototransistor
Osamu Azami et al.[ ]LocomotionCastingDragonSkin 30Fluidic actuator—air/waterPressure sensor, encoder
Saeed et al. [ ]Medical devices-Flexible silicone microtube, fiberPneumatic Artificial Muscles (McKibben)-
Khan et al.[ ]DriveCastingDragonSkin 10Fluidic actuator—airBend sensor, pressure
Li et al.[ ]LocomotionAssemblyAcrylic elastomer (3M—VHB), polyethylene terephthalate (PET), electros—carbon greaseDielectric elastomer (DE)-
Pengfei Yang et al. [ ]LocomotionCastingEcoflex 00-30, paperFluidic actuator—air (PNs)-
Digumarti et al. [ ]LocomotionCastingDragon Skin 10 SLOW, Sil-Poxy, Silk-Pig pigmentsFluidic actuator—air-
Kang et al. [ ]Medical devices, rehabilitationCastingPolymer (KE-1300T)Actuator with cablesSensor PliancyHand Mat (Novel)
Chen et al. [ ]DriveCastingELASTOSIL RT 622 A, methyl methacrylate, Kevlar fibers, glassFluidic actuator—airFlexible sensor
Wang et al. [ ]Locomotion3D printingElastomerFluidic actuator—air-
Jiang et al. [ ]Locomotion3D printing, (FDM) flexoskeleton Acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), polycarbonate (PC), adhesive (cyanoacrylate)Microservomotors-
Bützer et al. [ ]Medical devices, rehabilitation3D printing, sssemblyIP 600 (Igus), stainless steel bands, leaf springs (Precisinox SRL)Actuator with cablesBend sensor, force, EMG, EEG
Li et al. [ ]Control3D printingPolymerShape Memory Actuator (SMA)Displacement sensor
Li et al.[ ]DriveCastingSilicone rubber (HC—920), thermoplastic polyurethane (TPU), fibersFluidic actuator—air-
Yang et al. [ ]DriveCastingEcoflex 00-50, glass particles, paperFluidic actuator—air-
Paternò et al. [ ]ManipulationCastingEcoflex 00-30, Ecoflex 00-50Fluidic actuator—airPressure sensor
Moghadam et al.[ ]LocomotionLaser cuttingThermoplastic polyurethane (TPU)Fluidic actuator—airPressure sensor
Tang et al. [ ]LocomotionCasting, 3D printingEcoflex 00-50, polylactic acid (PLA)Fluidic actuator—airPressure sensor
Sayed et al. [ ]LocomotionCasting, laser cuttingDragon Skin 10, 20, 30, Ecoflex 00-10, 30, 50, acrylic polymerFluidic actuator—air, electromagnetic inductionPressure sensor, temperature, omnidirectional sound, distance
Wang et al.[ ]LocomotionAssemblyLatex, cotton fiber, polyester fiberArtificial pneumatic muscles (Curl—CPAM)Pressure sensor
Niu et al.[ ]LocomotionCastingEcoflex 00-50Magnetic actuator-
Nguyen et al. [ ]SensoryCastingDragon-Skin 00-30, nylon fibersFluidic actuator—airPressure sensor, laser, soft sensor
Deng et al. [ ]Locomotion, manipulation3D printingEcoflex 00-30Fluidic actuator—airPressure sensor
Thuruthel et al. [ ]Manipulation-ElastomerFluidic actuator—air-
Singh et al. [ ]ManipulationCastingPolyamide (PA12)Fluidic actuator—airOptical sensor, video camera, potentiometers
Eder et al. [ ]ManipulationAssemblyElastomerPneumatic Artificial Muscles (PAM)Pressure sensor, stretch, gyroscope, 6D accelerometer
Burns et al. [ ]Medical devices, rehabilitationAssemblyTextiles (Glove)Actuator with cablesEMG, flexible sensor
Li et al. [ ]LocomotionAssemblyThin steel, elastomerShape Memory Actuator (SMA)Flexible sensor
Berg et al.[ ]LocomotionCasting, assembly, 3D printingPolylactic acid (PLA), silicon, nylon, PETG, nitrile rubber, POMActuator with cables-
Coad et al. [ ]Exploration3D printingPolythene (LDPE)Fluidic actuator—air-
Heung et al. [ ]Medical devices, rehabilitationCastingDragon Skin 30, start stainless steelFluidic actuator—air-
Bhattachara et al. [ ]Medical devicesCastingEcoflex 00-30Fluidic actuator—airPressure, Force Sensing Potentiometer (FSP)
Chen et al. [ ]LocomotionCastingEcoflex 00-30Fluidic actuator—airIMU
Shintake et al. [ ]LocomotionCasting, pad printingPolymethyl methacrylate (PMMA), polyethylene (PET), Nusil CF19-2186, Sylgard 184, Sylgard RTV-734, carbon blackElastomer dielectric-
Liu et al. [ ]Prehension3D printing, assemblyABS, latex, leaf springFluidic actuator—air—hybrid (FHPA)Force sensor—6D, gyroscope.
Kim et al. [ ]Manipulation3D printing, castingDragon-Skin 10, polymerFluidic actuator—airEGaIn
Nguyen et al. [ ]Manipulation3D printing, castingABS, Dragon-Skin 30, Sil-PoxyFluidic actuator—airPressure sensor, IMU, EMG
Hoang et al.[ ]Prehension3D printing, castingEcoflex 00-30, carbon greaseFluidic actuator—airEGaIn, pressure, force
Dang et al. [ ]Medical devices3D printing, castingEcoflex 00-30, Sil-Poxy, cyanoacrylate, beeswaxFluidic actuator—airPressure sensor
Ji et al.[ ]Locomotion3D printingFilaments NinjaFlexActuator with cablesIMU, TOF (Time of Flight) sensor
Gharavi et al.[ ]Prehension, rehabilitation.3D printing, castingSilicone RTV-2 325, reinforced with fibersFluidic actuator—airBend sensor
Wu et al.[ ]Locomotion3D printing (stereolithography), castingEcoflex T606, metal powders (Nd2Fe14B)Magnetic actuator-
Wu et al. [ ]LocomotionCastingSilicone rubber, polyacrylateFluidic actuator—airMagnetometer sensor (3 axes)
O’Neill et al. [ ]Medical devicesAssemblyTextile, polyurethaneFluidic actuator—airTorque sensor, pressure
Li et al. [ ]Locomotion3D printing, castingSilicone rubber, thermoplastic urethane (TPU), nylon fibers, fibersFluidic actuator—air, actuator with cablesForce sensor, pressure
Zhang et al. [ ]LocomotionCasting, 3D printingEcoflex 00-50, Ecoflex 00-30, nylon fibersFluidic actuator—airPressure sensor, force, electromagnetic tracking sensor (EM—6 DOF)
Horvath et al. [ ]Medical devicesCasting, 3D printingMedical mesh (Parietex), lycra, velcro, Dragon-Skin FX-ProShape Memory Actuator (SMA)Pressure sensor
Liu et al. [ ]Prehension3D printingThermoplastic elastomer (BootFeeder)DC motor (Sumotor 37GARH)Force sensor
Cao et al. [ ]LocomotionAssembly, castingMembrane VHB 4910, carbon grease, polyethylene terephthalate (PET)Dielectric elastomer (DEA), electroadhesion-
Hofer et al. [ ]ManipulationAssembly, 3D printing, laser cuttingFabric poplin, thermoplastic polyurethane (TPU), thermoplastic adhesive, velcroFluidic actuator—airPressure sensor

Analysis of bibliographic references according to the materials.

Ref.MaterialFeaturesFunctionalityApplication
[ ]SMP, CuNi, Ecoflex 20, Silgard184 with silk threads and particlesHigh pressures and forcesActuator, sensorLocomotion
[ ]Dragon Skin 10, 30, Ecoflex -30Fiber-reinforcedActuatorBiomimetic
[ ]Ecoflex 00–50, Dragon skin 30Fiber-reinforced—KevlarActuatorManipulation
[ ]Silicone rubber (XIAMETER RTV-4234-T4)-ActuatorRehabilitation
[ ]Dragon Skin 30, Ecoflex 00-30Materials with different elasticity to provide stability to the modulesCasing, actuatorLocomotion
[ ]Dragon Skin 30, rubber, nylonReinforced with glass fiberActuatorPrehension
[ ]Dragon Skin 10, 30Fiber-reinforced, Shore A hardness of 10, 30ActuatorManipulation, prehension
[ ]Ecoflex 00-30Low RI (refractive index)Actuator, main bodyMedical devices
[ ]Conductive carbon grease, PET, flexible plastic, VHB_ 4910Constructive simplicityStructure, actuatorGrasping devices, manipulation
[ ]Ecoflex 00–50, 00-30, butadiene rubber, fiberglassActuators reinforced with glass fibersActuatorLocomotion
[ ]Elastosil M4601, Ecoflex 00–30-ActuatorDrive, prehension
[ ]Silicon Ecoflex 00-10Low elastic modulus, high elongation at breakMixing polymer materialLocomotion
[ ]FilaFlex—thermoplastic elastomerHigh elasticity, abrasion resistance, low modulus of elasticityRobot bodyDrive, prehension
[ ]Lycra fibers, latex, liquid silicone, silicone paintIt gives the robot fish mobility, waterproofing, and toughnessOuter layerLocomotion
[ ]Poplin, thermoplastic polyurethane (TPU) High tensile and tensile strengthStructure of actuatorsManipulation
[ ]Polyamide 12High tensile strength, low densityEnd effectorDrive
[ ]Acrylic elastomer (3M—VHB), polyethylene terephthalate (PET), electros—carbon greaseGood compliance, flexibility, manufacturing, and actuationRobot bodyLocomotion
[ ]Ecoflex 00-30-ActuatorLocomotion
[ ]Polymer (KE-1300T)Low deformabilityActuator bodyMedical devices
[ ]Thermoplastic polyurethane (TPU)Inexpensive commercially available materialsRobot bodyLocomotion
[ ]Ecoflex 00-50, polylactic acid (PLA)-Robot bodyLocomotion.
[ ]Polymethyl methacrylate (PMMA), polyethylene (PET), Nusil CF19-2186, Sylgard 184, Sylgard RTV-734, carbon black-Robot body, actuatorLocomotion
[ ]ABS, Dragon-Skin 30, Sil-PoxyReinforced with plastic ringsRobot bodyManipulation
[ ]Ecoflex 00-30, Sil-Poxy, cyanoacrylate, beeswaxComposed of 4 segmentsBody simulatorMedical devices
[ ]Silicone rubber, thermoplastic urethane (TPU),nylon fibers, fibersFiber-reinforcedLeg structureLocomotion
[ ]Fabric poplin, thermoplastic polyurethane (TPU), thermoplastic adhesive, velcroBonding the layers with a heat pressActuatorManipulation

Analysis of bibliographic references according to the actuators.

Ref.Actuator TypeMode of DrivingDriving SystemPower DensityWeightApplicationLimitations/ChallengesCharacteristics
[ ]Fluidic actuator—airPressurizationArduino Uno, EGaIn sensor, servo motor-0.45 gLocomotionInfluence of wires and connecting tubes on locomotionStructure made up of 5 layers with thicknesses of 20 μm
[ ]Fluidic actuator—waterPressurizationPump, pressure sensor, flow IMU, stepper motor-432 gBiomimetics-Semi-round siphons
[ ]Fluidic actuator—airPressurizationPump, pressure sensor, Arduino UNO, EMG--Manipulation-Designed based on human fingers
[ ]Fluidic actuator—deionized waterPressurization3 mL syringes, stepper motors, TMCM-6214 controller--Medical devices-Performing rotational and translational movements
[ ]Fluidic actuator—airPressurization, vacuumedPump, solenoid valves, proportional valves, microcontroller, pressure/vacuum sensor, IMU--Medical devices, rehabilitation-Actuator with semi-rigid segments
[ ]Fluidic actuator Pressurization, vacuumedPump, solenoid valve, actuator, pressure sensor-~50-90 g / moduleLocomotionLimited applications due to connecting tubes, no feedback loopCaterpillar-like locomotion and reconfigurable structure
[ ]Fluidic actuator—airPressurization, vacuumedAir pump, vacuum, Arduino UNO, bending sensor, force, ultrasonic, pressure; proportional valve--PrehensionClamping of parts with limited dimensionsReinforced with glass fiber
[ ]Fluidic actuator—airPressurizationClosed-loop control based on stereo camera and video camera-1050 gManipulation, prehensionClosed-loop control due to the non-linear characteristics of the materialThree degrees of freedom, reinforced with Kevlar fibers
[ ]Fluidic actuator—airPressurizationControl using graphical user interface (GUI) and feedback from optical sensors in the form of force--Medical devicesReduction in thickness and outer diameterThree actuators positioned circularly with an angle of 120º
[ ]Elastomer dielectric—electricElectricProgrammable automatic—(PLC), relay, EMCO amplifier-18 gManipulationReduced handling forceSimple and cheap construction
[ ]Electrostatic servo motor, VASAVacuumed, electricVacuum regulator, digital sensor, laser-43 gLocomotionLimited autonomy due to connection to external energy sourcesVersatile, fast, and efficient locomotion
[ ]Rotary fluidic actuator—airPressurizationRegulator, electropneumatic valve, pump--Medical devices, rehabilitationPlacing the device on the patient’s torsoRehabilitation of hip flexion
[ ]Fluidic actuator—airPressurization--460 gPrehensionGrasping of sharp elementsVariable stiffness using passive particle locking
[ ]Electromagnetic actuatorElectromagneticMagnetic foot control, tilt angle measurement with Matlab-0.23 gLocomotionAttachment of the robot leg mechanism to the substrateAverage locomotion speed of 3.1 mm/s
[ ]Elastomer dielectric—DEElectricPower supply, signal generator, voltage amplifier9 mW/g4.9 gLocomotionFulfilling the characteristics of autonomyConstructive simplicity
[ ]Pneumatic musclesPressurizationCompressor, valve, pressure sensor, Bluetooth module, EMG, MCU (PIC18F46K22)-208 gMedical devices, rehabilitation-Based on the control of EMG signals
[ ]Pneumatic Artificial Muscles (PAM)PressurizationController, air pump, battery, solenoid valve-1.5 kgPrehensionLow grip speedClamping autonomy of 300 cycles
[ ]Fluidic actuator—airPressurization---Drive, prehension-Making bending and twisting movements by varying the camera angle
[ ]Dielectric Elastomer (DEA), flexible electroadhesive (EA)ElectricHigh voltage amplifier, power supply, MOSFET, Arduino UNO-12 gLocomotionHigh voltage levelsCrawling on vertical surfaces at a speed of 2.3 mm/s at a frequency of 0.8 Hz
[ ]MagneticactuatorMagnetic fields6 electromagnetic coils, video camera--LocomotionInvestigating locomotion in a straight line onlyLack of radiation and not connecting the robot with cables or wires
[ ]Fluidic actuator—air (cuboid, arched)PressurizationArduino Mega 2560, HC-12 module, air pump, battery, solenoid valve, pressure regulator, CO2 tank, motion sensor, pressure-1.29 kgLocomotionReducing the overall size and weight of the robot torsoReinforced with Kevlar fibers
[ ]Helicoidal fluidic actuator (SoPHIA)Pressurization--95 gMedical devicesImplementation of soft sensors to take information from the robotWrapped in polyester fabric
[ ]Shape Memory Actuator (SMA)Electromagnetic.Signal generator, linear amplifier, coils-7 gLocomotionImpedance variation limited to the power amplifierStrong magnetic field for activating SMA wires
[ ]Rotary fluidic actuator—airPressurizationPump, proportional valve, pressure sensor, rotary encoder-300 gActuators-Payload of 18.5 N·m at 180 kPa pressure
[ ]Shape Memory Actuator (SMA)ElectricFlexible sensor, current, analog/digital converter, SMA fire, microcontroller--LocomotionProtection of the robot at the temperature of the SMAThe SMA temperature can reach up to 90 °C
[ ]Rotary fluidicactuator—airPressurization, vacuumedAir pump, vacuum, Arduino Mega 2560 R3, proportional valves, pressure sensor, IMU-300 gManipulationBonding the component layers of the actuatorRotational articulation of the manipulator
[ ]Shape Memory Actuator (SMA)ElectricMOS amplifier, gyroscope sensor, linear encoder, microchip STM32 controller--ManipulationAdditional cooling methods to shorten SMA recovery timeNine degrees of freedom, good positioning
[ ]Shape Memory Actuator (SMA- flexinol)ElectricSMA coils, amplifier, Hall sensor, Arduino UNO, PC--Manipulation, locomotionAustenitic phase transition temperatureDurable, cheap, and accurate manipulator
[ ]Artificial muscles with soft microtubules (SMAM)PressurizationFlexible silicone microtube, micro-coil, optical encoder, syringe, micromotor, Matlab/Simulink-0.28 gMedical devicesThe non-linear adaptive control algorithmElongation by 245%
[ ]Wired actuatorElectricWires, stepper motor, micro-camera, Arduino microcontroller, electromagnetic tracking system, user interface (GUI)--Medical devicesControllability of the robot1.4 ± 0.4 mm positioning and 1.5 ± 1.1 degree orientation accuracy
[ ]Fluidic actuator—airPressurizationPump, pressure sensor, solenoid valve, Arduino--Devices to increase comfortThe distribution of the surface covered by the device is insufficientPressure distribution to ensure people’s comfort
[ ]ElastomerdielectricElectricPower supply, high voltage amplifier, relay, microcontroller, video camera-12.2 gLocomotionRolling speed, smooth locomotionRelatively high speed of the robot—0.65 m/s
[ ]Actuator withcablesElectricActuator (IG-32GM 03TYPE), processor (TMS320F2808), Li-ion battery, pliancy sensorhand mat (Roman)-104 gMedical devicesFinger joint stiffnessThe cables are connected to the glove by tension springs
[ ]Actuator withcablesElectricDC motor (Maxon), motor drivers (ESCON), Arduino Yun Mini, bend sensor, EMG, battery-148 gMedical devices, rehabilitationGrasping objects where more dexterity is requiredWearable exoskeleton for daily activities, easy for users to accept
[ ]Fluidic actuator—airPressurizationElectropneumatic regulator--DriveLarge range of motion angleAir pressures up to 400 kPa
[ ]Fluidic actuator—airPressurizationPneumatic pump, solenoid valves, valves, Arduino--Locomotion, manipulationThe problems regarding quantification of the deformations led to a failure to solve the model in its entiretyOpen-loop driving
[ ]Fluid actuator—airPressurizationMicro proportional regulator, Vicon tracking system, controller--ManipulationPositioning accuracy12 degrees of freedom (DOF)
[ ]Shape Memory Actuator (SMA)ElectricController (STM32), flexible sensor, voltage amplifier, wireless module, PC-100 gLocomotion-Closed-loop control
[ ]Fluidic actuator—airPressurizationPump, solenoid valve-207 gMedical devices, rehabilitationReduced degree of autonomyFiber-reinforced
[ ]Fluidic actuator—airPressurizationCompressor, proportional valves, Raspberry Pi, AD/DA interfaces, pressure sensors, force--Medical devicesThe occurrence of corrosion in stentsThe ROSE actuator has 12 layers
[ ]Fluidic actuator—airPressurizationGUI, pump, valve, battery, microcontroller-830 gLocomotionReduced size and weightCube with a side of 10 cm
[ ]Elastomer dielectricElectricHigh voltage converter, microcontroller, C-MOS camera-4.4 gLocomotion-Swimming speed of 37.2 mm/s at 5 kV
[ ]Fluidic actuator—airPressurizationSolenoid valve, pressure sensors, IMU, EMG, joystick, controller-960 gManipulationConnecting the manipulator through cables and hosesControlled by joystick
[ ]Magnetic actuatorMagnetic fieldMagnet, camcorder--ManipulationDesigning the robot to perform operationsControlled by magnetic field
[ ]Fluidic actuator—airPressurization, vacuumedArduino Mega 1820, pump, solenoid valves, PC, electromagnetic hatching (EM) sensor, pressure sensor, camera-14.5 gLocomotionReduced detection and handling capabilityAbility to handle a load 10 times its weight
[ ]Fluidic actuator—airPressurizationSolenoid valves, pressure sensor, Labjack T7 Pro, pressure regulator-126 gManipulationLimited range of motionActuator with three internal cavities (bellows) arranged circularly

Analysis of references according to the specific technologies.

Ref.TechnologyComponent in the StructureMaterialSizeBenefitsDisadvantages
[ ]3D—lithographyActuatorSilgard184 with silk threads and particles35 mm long, 12 mm wide, 1 mm thickParts with complex geometries-
[ ]CastingActuatorDragon Skin 10120 mm long, outer diameter 65 mm, hole diameter 16 mmComplex parts with internal cavities-
[ ]CastingActuatorSilicone rubber (XIAMETER RTV-4234-T4)-- -
[ ]Casting, 3D printingActuator—casting, mold—3D PrintingDragon Skin 30, Ecoflex 00-30Length 154 mmThree degrees of freedom (t-t-r), travel speed 18.5 m/h, rotation 1.63°/sLimited autonomy due to lack of feedback loop control
[ ]Casting, 3D printingActuatorDragon Skin 30, rubber, nylon---
[ ]CastingActuatorDragon Skin 10, 30540 mm long, 48 mm diameterPrecise positioning thanks to feedback controlHigh-complexity control system
[ ]CastingRobot body, actuatorEcoflex 00-30, VytaflexTM 20118 mm long, 62 mm wide, 3.5 mm thick--
[ ]Casting, assemblyActuatorConductive carbon grease, PET, flexible plastic, VHB_ 4910Length 320 mm, weight 18 gConstructive simplicityRelatively high actuation voltages
[ ]3D Printing—stereolithography (M-PSL)The body of the robotSpot E elastic, magnetic nanoparticles—EMG 1200Length 40 mmComposite print directly from a digital model-
[ ]3D printingActuatorFilaFlex—thermoplastic elastomerLength 180 mm, width 25 mmParts with complex geometriesSurface quality
[ ]CastingActuator, the body of the robotEcoflex 00-50Length 18 cm, width 10 cm, height 6 cm-Additional cost
[ ]3D printing, bonding by heat pressingActuatorPoplin, thermoplastic polyurethane (TPU), acrylonitrile butadiene styrene (ABS)-Fast and cheap model makingLife cycle unknown, optimal structure configuration
[ ]CastingActuatorEcoflex 00-50, Kevlar threads, adhesive (HJ-420)Length 100 mmElongation accuracy of 0.51 mmLack of flexible sensors
[ ]AssemblyRobot body (actuator)Acrylic elastomer (3M—VHB), polyethylene terephthalate (PET), electros—carbon greaseDiameter 106 mmRelatively high locomotion speedRelatively high voltage levels
[ ]3D printing—(FDM) flexoscheletThe skeleton of the robotAcrylonitrile butadiene styrene (ABS), polylactic acid (PLA), polycarbonate (PA), adhesive (cyanoacrylate)Leg length 70 mmFatigue resistance of parts is greatly improved-
[ ]Molding with built-in coreRobot body (actuator)Silicone rubber (HC—920), thermoplastic polyurethane (TPU), fibersLength 940 mm, width 35 mmIt allows the realization of actuators with a wide range of motion-
[ ]Laser cuttingRobot body (actuator)Thermoplastic polyurethane (TPU)Thickness 39 µmMaking robots in a relatively short timeLimited material types

Analysis of references according to the modelling methods.

Ref.Component in the StructureModeling MethodSizePower F/M
[ ]CFRD—central water flow regulation channelAnalytical, experimentalDiameter 16 mm--
[ ]Actuator (finger)Experimental, numerical---
[ ]ActuatorExperimental, numerical---
[ ]RobotExperimental, numericalLength 118 mm, width 62 mm, thickness 3.5 mm--
[ ]GripperAnalytical, experimental---
[ ]Robot structureExperimental, numericalLength 40 mm--
[ ]Robot structureExperimental, numericalLength 40 mm, width 10 mm9 mW/g-
[ ]SensorExperimental, numericalLength 93 mm, diameter 29 mm--
[ ]ActuatorAnalytical, numerical, experimentalLength 104 mm, height 14.5 mm, width 15 mm--
[ ]ActuatorExperimental, numericalLength 180 mm, width 25 mm--
[ ]Robot modeExperimental, numerical---
[ ]ActuatorAnalytical, experimental---
[ ]Actuator modeExperimental, numericalDiameter 80 mm, length 345 mm--
[ ]ActuatorExperimental, numericalLength 66.2 mm--
[ ]ActuatorExperimental, numerical---
[ ]ActuatorExperimental, numericalLength 100 mm--
[ ]ActuatorExperimental, numericalLength 940 mm, width 35 mm--

Analysis of bibliographic references according to the sensors.

Ref.Sensor TypePrincipleMaterialCharacteristicsApplication
[ ]EGaInResistance variation by changing the geometry of the microchannels with the elongation of the materialElastomer, eutectic, gallium, indiumIntegrate into the elastic structureClosed-loop control systems
[ ]Bending, force, pressure, ultrasonicThe variation in electrical resistance with the deformation of the structures-Integration into the actuator structureClosed-loop control systems
[ ]Soft optical sensorConverts the optical signal into forceVytaflex 20Core 100 mm long and 1x1 cross-sectionMedical applications
[ ]Liquid metal—galinstanResistance variation by changing the geometry of the microchannels with the elongation of the materialElastomer, eutectic, gallium, indiumChannels of square cross-section with 500 micron sidesClosed-loop control systems
[ ]EGaInResistance variation by changing the geometry of the microchannels with the elongation of the materialElastomer, eutectic, gallium, indiumChannel thickness of40 µmClosed-loop control systems
[ ]Bending, current, temperatureThe variation in electrical resistance with the deformation of the structures-Integration into the actuator structureMedicine, robotics
[ ]EGaInVariation in electrical resistance with sensor deformationEcoflex 00-50, elastomer, eutectic, gallium, indiumTactile abilityClosed-loop control systems
[ ]Bending, pressureThe variation in electrical resistance with the deformation of the structures-Mounting on the bottom layer of the actuatorClosed-loop control systems
[ ]BendingThe variation in light intensity through the materialMethyl methacrylate (PMMA)Integrated on the actuatorClosed-loop control systems
[ ]Soft sensorVariation in sensitivity at the time of variation in the morphology of the structureDragon Skin 00-30Integrated on the actuatorSensory systems based on rodent whiskers
[ ]Flexible sensorThe variation in electrical resistance with the deformation of the structuresPolyimideIntegrated on the exoskeletonMeasuring the angle of each joint
[ ]Flexible sensorThe variation in electrical resistance with the deformation of the structuresPolyamidePositioned on the robot structureClosed-loop robot locomotion
[ ]Force sensor—6D, gyroscopeVariation in electrical resistance-Positioned on the robot structureForce and angular displacement monitoring
[ ]EGaInThe variation in electrical resistance with the deformation of the structuresDragon Skin 10, eutectium, gallium, indiumPositioned on the robot structureAnti-collision detection sensor
[ ]Pressure sensor, IMU, EMGVariation in electrical resistance-Manipulator controlManipulation
[ ]EGaIn sensor, carbon greaseThe variation in electrical resistance with the deformation of the structuresEcoflex 00-30Integrated on the fingers of the gripperControl system
[ ]Bend sensorThe variation in electrical resistance with the deformation of the structuresRTV-2 325Integrated into the actuatorAngular variation as a function of pressure

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, D.-M.R. and S.-D.M.; methodology, D.-M.R. and F.M.; writing—original draft preparation, D.-M.R., S.-D.M. and A.I.-A.-D.; writing—review and editing, D.-M.R., O.-L.P. and C.-M.B.; supervision, S.-D.M. and C.-M.B.; funding acquisition, D.-M.R., S.-D.M. and A.I.-A.-D. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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Google DeepMind's Chatbot-Powered Robot Is Part of a Bigger Revolution

A multipleexposure photo of a binary code and the Google DeepMind logo

In a cluttered open-plan office in Mountain View, California, a tall and slender wheeled robot has been busy playing tour guide and informal office helper—thanks to a large language model upgrade, Google DeepMind revealed today . The robot uses the latest version of Google’s Gemini large language model to both parse commands and find its way around.

When told by a human “Find me somewhere to write,” for instance, the robot dutifully trundles off, leading the person to a pristine whiteboard located somewhere in the building.

Gemini’s ability to handle video and text—in addition to its capacity to ingest large amounts of information in the form of previously recorded video tours of the office—allows the “Google helper” robot to make sense of its environment and navigate correctly when given commands that require some commonsense reasoning. The robot combines Gemini with an algorithm that generates specific actions for the robot to take, such as turning, in response to commands and what it sees in front of it.

When Gemini was introduced in December, Demis Hassabis, CEO of Google DeepMind, told WIRED that its multimodal capabilities would likely unlock new robot abilities. He added that the company’s researchers were hard at work testing the robotic potential of the model.

In a new paper outlining the project, the researchers behind the work say that their robot proved to be up to 90 percent reliable at navigating, even when given tricky commands such as “Where did I leave my coaster?” DeepMind’s system “has significantly improved the naturalness of human-robot interaction, and greatly increased the robot usability,” the team writes.

A photo of a Google DeepMind employee interacting with an AI robot.

The demo neatly illustrates the potential for large language models to reach into the physical world and do useful work. Gemini and other chatbots mostly operate within the confines of a web browser or app, although they are increasingly able to handle visual and auditory input, as both Google and OpenAI have demonstrated recently. In May, Hassabis showed off an upgraded version of Gemini capable of making sense of an office layout as seen through a smartphone camera.

Academic and industry research labs are racing to see how language models might be used to enhance robots’ abilities. The May program for the International Conference on Robotics and Automation, a popular event for robotics researchers, lists almost two dozen papers that involve use of vision language models.

Investors are pouring money into startups aiming to apply advances in AI to robotics. Several of the researchers involved with the Google project have since left the company to found a startup called Physical Intelligence , which received an initial $70 million in funding; it is working to combine large language models with real-world training to give robots general problem-solving abilities. Skild AI , founded by roboticists at Carnegie Mellon University, has a similar goal. This month it announced $300 million in funding.

Just a few years ago, a robot would need a map of its environment and carefully chosen commands to navigate successfully. Large language models contain useful information about the physical world, and newer versions that are trained on images and video as well as text, known as vision language models, can answer questions that require perception. Gemini allows Google’s robot to parse visual instructions as well as spoken ones, following a sketch on a whiteboard that shows a route to a new destination.

In their paper, the researchers say they plan to test the system on different kinds of robots. They add that Gemini should be able to make sense of more complex questions, such as “Do they have my favorite drink today?” from a user with a lot of empty Coke cans on their desk.

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research paper in robot

Are We Ready to Investigate Robots? Issues and Challenges Involved in Robotic Forensics

  • Conference paper
  • First Online: 13 July 2024
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research paper in robot

  • Yash Patel   ORCID: orcid.org/0000-0002-8101-5006 39 &
  • Parag H. Rughani   ORCID: orcid.org/0000-0003-0243-4964 39  

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1194))

Included in the following conference series:

  • The International Conference on Recent Innovations in Computing

In the rapidly progressing era of robotics, their integration into daily lives is becoming ordinary. As beneficial as this integration is, the potential for misuse has raised alarms in the forensic community. The study delves into the complexity of forensic investigating incidents involving robots. Unlike traditional systems, robots possess both software and hardware components that are interconnected with their functionality, creating several challenges for forensic examination. From identifying various robot types to handling proprietary systems, the investigator’s task is daunting. Extracting and interpreting log data from robots present its hurdles, highlighted by a lack of specialized forensic tools tailored for robotic systems. Furthermore, the complex AI algorithms that drive many robots add another layer of complexity to a forensic investigator’s role. The paper also highlights jurisdictional challenges and emphasizes the pressing need for clear guidelines, policies, and Standard Operating Procedures (S.O.P.s) adapted to robotic forensics. A real-world case scenario in the paper offers a deep dive into the challenges faced during an actual robotic-related crime investigation. In essence, as society marches into a future dominated by robots, this research underscores the need not only for tools and methodologies to investigate them but also for robust design principles to ensure their secure operation.

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Patel, Y., Rughani, P.H. (2024). Are We Ready to Investigate Robots? Issues and Challenges Involved in Robotic Forensics. In: Singh, Y., Singh, P.K., Gonçalves, P.J.S., Kar, A.K. (eds) Proceedings of International Conference on Recent Innovations in Computing. ICRIC 2023. Lecture Notes in Electrical Engineering, vol 1194. Springer, Singapore. https://doi.org/10.1007/978-981-97-2839-8_18

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Google says Gemini AI is making its robots smarter

Deepmind is using video tours and gemini 1.5 pro to train robots to navigate and complete tasks..

By Jess Weatherbed , a news writer focused on creative industries, computing, and internet culture. Jess started her career at TechRadar, covering news and hardware reviews.

Share this story

A Google DeepMind worker training a robot to understand that Coke is his favorite soda.

Google is training its robots with Gemini AI so they can get better at navigation and completing tasks. The DeepMind robotics team explained in a new research paper how using Gemini 1.5 Pro ’s long context window — which dictates how much information an AI model can process — allows users to more easily interact with its RT-2 robots using natural language instructions.

This works by filming a video tour of a designated area, such as a home or office space, with researchers using Gemini 1.5 Pro to make the robot “watch” the video to learn about the environment. The robot can then undertake commands based on what it has observed using verbal and / or image outputs — such as guiding users to a power outlet after being shown a phone and asked “where can I charge this?” DeepMind says its Gemini-powered robot had a 90 percent success rate across over 50 user instructions that were given in a 9,000-plus-square-foot operating area.

Researchers also found “preliminary evidence” that Gemini 1.5 Pro enabled its droids to plan how to fulfill instructions beyond just navigation. For example, when a user with lots of Coke cans on their desk asks the droid if their favorite drink is available, the team said Gemini “knows that the robot should navigate to the fridge, inspect if there are Cokes, and then return to the user to report the result.” DeepMind says it plans to investigate these results further.

The video demonstrations provided by Google are impressive, though the obvious cuts after the droid acknowledges each request hide that it takes between 10–30 seconds to process these instructions, according to the research paper. It may take some time before we’re sharing our homes with more advanced environment-mapping robots , but at least these ones might be able to find our missing keys or wallets.

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MIT researchers introduce generative AI for databases

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A new tool makes it easier for database users to perform complicated statistical analyses of tabular data without the need to know what is going on behind the scenes.

GenSQL, a generative AI system for databases, could help users make predictions, detect anomalies, guess missing values, fix errors, or generate synthetic data with just a few keystrokes.

For instance, if the system were used to analyze medical data from a patient who has always had high blood pressure, it could catch a blood pressure reading that is low for that particular patient but would otherwise be in the normal range.

GenSQL automatically integrates a tabular dataset and a generative probabilistic AI model, which can account for uncertainty and adjust their decision-making based on new data.

Moreover, GenSQL can be used to produce and analyze synthetic data that mimic the real data in a database. This could be especially useful in situations where sensitive data cannot be shared, such as patient health records, or when real data are sparse.

This new tool is built on top of SQL, a programming language for database creation and manipulation that was introduced in the late 1970s and is used by millions of developers worldwide.

“Historically, SQL taught the business world what a computer could do. They didn’t have to write custom programs, they just had to ask questions of a database in high-level language. We think that, when we move from just querying data to asking questions of models and data, we are going to need an analogous language that teaches people the coherent questions you can ask a computer that has a probabilistic model of the data,” says Vikash Mansinghka ’05, MEng ’09, PhD ’09, senior author of a paper introducing GenSQL and a principal research scientist and leader of the Probabilistic Computing Project in the MIT Department of Brain and Cognitive Sciences.

When the researchers compared GenSQL to popular, AI-based approaches for data analysis, they found that it was not only faster but also produced more accurate results. Importantly, the probabilistic models used by GenSQL are explainable, so users can read and edit them.

“Looking at the data and trying to find some meaningful patterns by just using some simple statistical rules might miss important interactions. You really want to capture the correlations and the dependencies of the variables, which can be quite complicated, in a model. With GenSQL, we want to enable a large set of users to query their data and their model without having to know all the details,” adds lead author Mathieu Huot, a research scientist in the Department of Brain and Cognitive Sciences and member of the Probabilistic Computing Project.

They are joined on the paper by Matin Ghavami and Alexander Lew, MIT graduate students; Cameron Freer, a research scientist; Ulrich Schaechtle and Zane Shelby of Digital Garage; Martin Rinard, an MIT professor in the Department of Electrical Engineering and Computer Science and member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Feras Saad ’15, MEng ’16, PhD ’22, an assistant professor at Carnegie Mellon University. The research was recently presented at the ACM Conference on Programming Language Design and Implementation.

Combining models and databases

SQL, which stands for structured query language, is a programming language for storing and manipulating information in a database. In SQL, people can ask questions about data using keywords, such as by summing, filtering, or grouping database records.

However, querying a model can provide deeper insights, since models can capture what data imply for an individual. For instance, a female developer who wonders if she is underpaid is likely more interested in what salary data mean for her individually than in trends from database records.

The researchers noticed that SQL didn’t provide an effective way to incorporate probabilistic AI models, but at the same time, approaches that use probabilistic models to make inferences didn’t support complex database queries.

They built GenSQL to fill this gap, enabling someone to query both a dataset and a probabilistic model using a straightforward yet powerful formal programming language.

A GenSQL user uploads their data and probabilistic model, which the system automatically integrates. Then, she can run queries on data that also get input from the probabilistic model running behind the scenes. This not only enables more complex queries but can also provide more accurate answers.

For instance, a query in GenSQL might be something like, “How likely is it that a developer from Seattle knows the programming language Rust?” Just looking at a correlation between columns in a database might miss subtle dependencies. Incorporating a probabilistic model can capture more complex interactions.   

Plus, the probabilistic models GenSQL utilizes are auditable, so people can see which data the model uses for decision-making. In addition, these models provide measures of calibrated uncertainty along with each answer.

For instance, with this calibrated uncertainty, if one queries the model for predicted outcomes of different cancer treatments for a patient from a minority group that is underrepresented in the dataset, GenSQL would tell the user that it is uncertain, and how uncertain it is, rather than overconfidently advocating for the wrong treatment.

Faster and more accurate results

To evaluate GenSQL, the researchers compared their system to popular baseline methods that use neural networks. GenSQL was between 1.7 and 6.8 times faster than these approaches, executing most queries in a few milliseconds while providing more accurate results.

They also applied GenSQL in two case studies: one in which the system identified mislabeled clinical trial data and the other in which it generated accurate synthetic data that captured complex relationships in genomics.

Next, the researchers want to apply GenSQL more broadly to conduct largescale modeling of human populations. With GenSQL, they can generate synthetic data to draw inferences about things like health and salary while controlling what information is used in the analysis.

They also want to make GenSQL easier to use and more powerful by adding new optimizations and automation to the system. In the long run, the researchers want to enable users to make natural language queries in GenSQL. Their goal is to eventually develop a ChatGPT-like AI expert one could talk to about any database, which grounds its answers using GenSQL queries.   

This research is funded, in part, by the Defense Advanced Research Projects Agency (DARPA), Google, and the Siegel Family Foundation.

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Title: mobility vla: multimodal instruction navigation with long-context vlms and topological graphs.

Abstract: An elusive goal in navigation research is to build an intelligent agent that can understand multimodal instructions including natural language and image, and perform useful navigation. To achieve this, we study a widely useful category of navigation tasks we call Multimodal Instruction Navigation with demonstration Tours (MINT), in which the environment prior is provided through a previously recorded demonstration video. Recent advances in Vision Language Models (VLMs) have shown a promising path in achieving this goal as it demonstrates capabilities in perceiving and reasoning about multimodal inputs. However, VLMs are typically trained to predict textual output and it is an open research question about how to best utilize them in navigation. To solve MINT, we present Mobility VLA, a hierarchical Vision-Language-Action (VLA) navigation policy that combines the environment understanding and common sense reasoning power of long-context VLMs and a robust low-level navigation policy based on topological graphs. The high-level policy consists of a long-context VLM that takes the demonstration tour video and the multimodal user instruction as input to find the goal frame in the tour video. Next, a low-level policy uses the goal frame and an offline constructed topological graph to generate robot actions at every timestep. We evaluated Mobility VLA in a 836m^2 real world environment and show that Mobility VLA has a high end-to-end success rates on previously unsolved multimodal instructions such as "Where should I return this?" while holding a plastic bin. A video demonstrating Mobility VLA can be found here: this https URL
Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI)
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Designing autonomous robots for use on Mars and closer to home

Preparing to engage the robot during the MDRS mission.

Pawel Sawicki (BioMedEngr MS’22, AeroEngr PhD’23) is exploring the barren landscape of Mars and testing out critical new technologies through a one-of-a-kind experience here on Earth.

Welcome to the Mars Desert Research Station, an “analog” astronaut research facility in the remote Utah desert. Operated by the Mars Society, the center gives scientists and engineers the opportunity to test out future space experiments without a long space journey.

Sawicki, a University of Colorado Boulder master’s and PhD alumnus, recently returned from the base, where he spent two weeks as a mission commander with a six-member crew. The team lived and worked under conditions remarkably similar to what NASA astronauts will face on the red planet.

“It was pretty exciting. We lived in the station and to go outside we had to wear EVA suits,” Sawicki said. “We’re simulating life on Mars so we can learn how to design experiments, equipment, and operations for when astronauts really go and face that challenge.”

Along with a series of geological and nuclear experiments was a 30 lb., four-wheel, ground robot provided by Nisar Ahmed, an associate professor of aerospace at the University of Colorado Boulder.

Robots will be important on future Mars missions, but only if users can easily understand their capabilities and limitations, said Nick Conlon, one of Ahmed’s PhD students in the Ann and H.J. Smead Department of Aerospace Engineering Sciences.

Ahmed’s lab is focused on developing methods so a robot can accurately tell operators how well it will be able to do a task. Called Factorized Machine Self-Confidence, the system will give users an easy way to grasp how competent the robot is.

“The objective was to use the robot to take video autonomously in different areas to create a 360 view of the environment, like Google Maps Street View,” Conlon said. “Before the robot starts as task, it analyzes its internal models to report if it can achieve the goal. Can it drive to a certain area, does it have enough battery to get back, can it avoid obstacles? Things like that.”

Conlon demonstrating the robot prior to the mission beginning.

While astronauts are likely to be highly trained on their equipment, the goal of this robotics research is to make it possible for regular users to utilize the technology with little trouble.

“People have different ideas of what a robot might be capable of,” Conlon said. “We don’t want them to over trust a piece of equipment and break it or get hurt or drive off a cliff. We also don’t want people to under trust and have it sit and collect dust in a corner. We want people to use it within its limits and want to use it.”

Conlon said much of the research with the robot thus far has been in controlled environments, making Sawicki’s MDRS mission a unique deployment opportunity.

“We’ll be writing a paper from all we’ve learned from this experience,” Sawicki said. “One of the key findings is just how to make the system super robust for a field study, taking it on an EVA, and wearing a spacesuit in the process.”

Although there were some early diagnostic issues, the robot was able to complete all of the requested site surveys, and both Conlon and Sawicki are hopeful the data will be helpful for subsequent MDRS missions.

One unique challenge that will face future Mars astronauts is communicating with home. Due to the massive distance between the red planet and Earth, one way transmissions have a minimum delay of 8-10 minutes. That makes any live calls impossible. The same restrictions are imposed on the analogue astronauts.

“The isolation was definitely a mental challenge. Nick was back in Colorado and when I had to work with him on an issue with the robot, there are no phone calls and you can’t exchange messages quickly. You send an email and wait,” Sawicki said.

Participating in an MDRS mission fulfilled a goal Sawicki had held since his time as a grad student. CU Boulder offers a course called Medicine in Space and Surface Environments that takes students to MDRS, but during his PhD program Sawicki was unable to make it work with his schedule.

He reached out to MDRS after graduating to sign up for a mission on his own and they offered the opportunity to be mission commander.

“My PhD was in hypersonics but I had taken all of these bioastronautics classes and they said you’re a great fit for this mission,” Sawicki said. “I learned the trials and tribulations of what goes into an isolated mission like this, maintaining crew stability, scheduling. It was a great learning experience for me, and a unique opportunity for Ahmed and Conlon to learn about how future astronauts may one day work with, and alongside, autonomous robots.”

The MDRS 297 mission patch.

The MDRS 297 mission patch, with the team member names and the robot in lower left.

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The Robot Report

NVIDIA Research to present simulation, generative AI advances at SIGGRAPH

By Aaron Lefohn | July 12, 2024

NVIDIA Research today said it is bringing an array of advancements in rendering, simulation, and generative AI to SIGGRAPH 2024. The computer graphics conference will be from July 28 to Aug. 1 in Denver.

At SIGGRAPH , NVIDIA Corp. plans to present more than 20 papers introducing innovations advancing synthetic data generators and inverse rendering tools that can help train next-generation models. The company said its AI research is making simulation better by boosting image quality and unlocking new ways to create 3D representations of real or imagined worlds.

The papers focus on diffusion models for visual generative AI , physics-based simulation and increasingly realistic AI -powered rendering. They include two technical Best Paper Award winners and collaborations with universities across the U.S., Canada, China, Israel, and Japan, as well as researchers at companies including Adobe and Roblox.

These initiatives will help create tools that developers and businesses can use to generate complex virtual objects, characters, and environments, said the company. Synthetic data generation can then be harnessed to tell powerful visual stories, aid scientists’ understanding of natural phenomena or assist in simulation-based training of robots and autonomous vehicles.

SITE AD for the 2024 RoboBusiness registration now open.

Diffusion models improve texture painting, text-to-image generation

Diffusion models, a popular tool for transforming text prompts into images, can help artists, designers and other creators rapidly generate visuals for storyboards or production, reducing the time it takes to bring ideas to life.

Two NVIDIA-authored papers are advancing the capabilities of these generative AI models.

ConsiStory , a collaboration between researchers at NVIDIA and Tel Aviv University, makes it easier to generate multiple images with a consistent main character. The company said it is an essential capability for storytelling use cases such as illustrating a comic strip or developing a storyboard. The researchers’ approach introduces a technique called subject-driven shared attention, which reduces the time it takes to generate consistent imagery from 13 minutes to around 30 seconds.

NVIDIA researchers last year won the Best in Show award at SIGGRAPH’s Real-Time Live event for AI models that turn text or image prompts into custom textured materials. This year, they are presenting a paper that applies 2D generative diffusion models to interactive texture painting on 3D meshes, enabling artists to paint in real time with complex textures based on any reference image.

ConsiStory makes it easier to generate multiple images with the same character, says NVIDIA Research.

ConsiStory makes it easier to generate multiple images with the same character. Source: NVIDIA Research

NVIDIA Research kick-starts developments in physics-based simulation

Graphics researchers are narrowing the gap between physical objects and their virtual representations with physics-based simulation — a range of techniques to make digital objects and characters move the same way they would in the real world. Several NVIDIA Research papers feature breakthroughs in the field, including SuperPADL, a project that tackles the challenge of simulating complex human motions based on text prompts.

Using a combination of reinforcement learning and supervised learning, the researchers demonstrated how the SuperPADL framework can be trained to reproduce the motion of more than 5,000 skills — and can run in real time on a consumer-grade NVIDIA GPU.

Another NVIDIA paper features a neural physics method that applies AI to learn how objects — whether represented as a 3D mesh, a NeRF or a solid object generated by a text-to-3D model — would behave as they are moved in an environment. A NeRF, or neural radiance field, is an AI model that takes 2D images representing a scene as input and interpolates between them to render a complete 3D scene.

A paper written in collaboration with Carnegie Mellon University discusses the development of develops a new kind of renderer. Instead of modeling physical light, the renderer can perform thermal analysis, electrostatics, and fluid mechanics (see video below). Named one of five best papers at SIGGRAPH, the method is easy to parallelize and doesn’t require cumbersome model cleanup, offering new opportunities for speeding up engineering design cycles.

Additional simulation papers introduce a more efficient technique for modeling hair strands and a pipeline that accelerates fluid simulation by 10x.

Papers raise the bar for realistic rendering, diffraction simulation

Another set of NVIDIA-authored papers will present new techniques to model visible light up to 25x faster and simulate diffraction effects — such as those used in radar simulation for training self-driving cars — up to 1,000x faster.

A paper by NVIDIA and University of Waterloo researchers tackles free-space diffraction , an optical phenomenon where light spreads out or bends around the edges of objects. The team’s method can integrate with path-tracing workflows to increase the efficiency of simulating diffraction in complex scenes, offering up to 1,000x acceleration. Beyond rendering visible light, the model could also be used to simulate the longer wavelengths of radar, sound or radio waves.

Path tracing samples numerous paths — multi-bounce light rays traveling through a scene — to create a photorealistic picture. Two SIGGRAPH papers improve sampling quality for ReSTIR, a path-tracing algorithm first introduced by NVIDIA and Dartmouth College researchers at SIGGRAPH 2020 that has been key to bringing path tracing to games and other real-time rendering products.

One of these papers, a collaboration with the University of Utah, shares a new way to reuse calculated paths that increases effective sample count by up to 25x , significantly boosting image quality. The other improves sample quality by randomly mutating a subset of the light’s path. This helps denoising algorithms perform better, producing fewer visual artifacts in the final render.

NVIDIA and University of Waterloo researchers have developed techniques to mitigate free-space diffraction in complex scenes.

NVIDIA and University of Waterloo researchers have developed techniques to mitigate free-space diffraction in complex scenes. Source: NVIDIA Research

Teaching AI to think in 3D

NVIDIA researchers are also showcasing multipurpose AI tools for 3D representations and design at SIGGRAPH.

One paper introduces fVDB , a GPU-optimized framework for 3D deep learning that matches the scale of the real world. The fVDB framework provides AI infrastructure for the large spatial scale and high resolution of city-scale 3D models and NeRFs , and segmentation and reconstruction of large-scale point clouds.

A Best Technical Paper award winner written in collaboration with Dartmouth College researchers introduces a theory for representing how 3D objects interact with light. The theory unifies a diverse spectrum of appearances into a single model.

In addition, a NVIDIA Research collaboration with the University of Tokyo, the University of Toronto, and Adobe Research introduces an algorithm that generates smooth, space-filling curves on 3D meshes in real time. While previous methods took hours, this framework runs in seconds and offers users a high degree of control over the output to enable interactive design.

See NVIDIA Research at SIGGRAPH

NVIDIA events at SIGGRAPH will include a fireside chat between NVIDIA founder and CEO Jensen Huang and Lauren Goode, senior writer at Wired , on the impact of robotics and AI in industrial digitalization.

NVIDIA researchers will also present OpenUSD Day by NVIDIA , a full-day event showcasing how developers and industry leaders are adopting and evolving OpenUSD to build AI-enabled 3D pipelines.

NVIDIA Research has hundreds of scientists and engineers worldwide, with teams focused on topics including AI, computer graphics, computer vision, self-driving cars, and robotics.

About the author

Aaron Lefohn leads the Real-Time Rendering Research team at NVIDIA. He has led real-time rendering and graphics programming model research teams for over a decade and has productized many research ideas into games, film rendering, GPU hardware, and GPU APIs.

Lefohn’s teams’ inventions have played key roles in bringing ray tracing to real-time graphics, combining AI and computer graphics, and pioneering real-time AI computer graphics. Some of the NVIDIA products derived from the teams’ inventions include DLSS, RTX Direct Illumination (RTXDI), NVIDIA’s Real-Time Denoisers (NRD), the OptiX Deep Learning Denoiser, and more.

The teams’ current focus areas include real-time physically-based light transport, AI computer graphics, image metrics, and graphics systems.

Lefohn previously worked in rendering R&D at Pixar Animation Studios, creating interactive rendering tools for film artists. He was also part of a graphics startup called Neoptica creating rendering software and programming models for Sony PlayStation 3. In addition, Lefohn led real-time rendering research at Intel. He received his Ph.D. in computer science from UC Davis, his M.S. in computer science from the University of Utah, and an M.S. in theoretical chemistry.

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COMMENTS

  1. The International Journal of Robotics Research: Sage Journals

    International Journal of Robotics Research (IJRR) was the first scholarly publication on robotics research; it continues to supply scientists and students in robotics and related fields - artificial intelligence, applied mathematics, computer science, electrical and mechanical engineering - with timely, multidisciplinary material... This journal is peer-reviewed and is a member of the ...

  2. Reinforcement learning for robot research: A comprehensive review and

    Reinforcement learning (RL), 1 one of the most popular research fields in the context of machine learning, effectively addresses various problems and challenges of artificial intelligence. It has led to a wide range of impressive progress in various domains, such as industrial manufacturing, 2 board games, 3 robot control, 4 and autonomous driving. 5 Robot has become one of the research hot ...

  3. Review of Robotics Technologies and Its Applications

    Abstract: Robots are automatic equipment integrating advanced technologies in multiple disciplines such as mechanics, electronics, control, sensors, and artificial intelligence. Based on a brief introduction of the development history of robotics, this paper reviews the classification of the type of robots, the key technologies involved, and the applications in various fields, analyze the ...

  4. (PDF) ARTIFICIAL INTELLIGENCE IN ROBOTICS: FROM ...

    This research paper explores the integration of artificial intelligence (AI) in robotics, specifically. focusing on the transition from automation to autonomous systems. The paper provides an ...

  5. Advancements in Humanoid Robots: A Comprehensive Review and Future

    This paper provides a comprehensive review of the current status, advancements, and future prospects of humanoid robots, highlighting their significance in driving the evolution of next-generation industries. By analyzing various research endeavors and key technologies, encompassing ontology structure, control and decision-making, and perception and interaction, a holistic overview of the ...

  6. Advances and perspectives in collaborative robotics: a ...

    The paper explores the current state of HRI research and the challenges faced in modeling and controlling robot behavior in physical and cognitive aspects to improve human-robot collaboration. The author discusses the need for robots to have cognitive capabilities to facilitate communication and collaboration with humans in different ...

  7. Journal of Robotics

    Online ISSN: 1687-9619. Print ISSN: 1687-9600. Journal of Robotics publishes original research articles as well as review articles on all aspects of automated mechanical devices, from their design and fabrication, to testing and practical implementation. As part of Wiley's Forward Series, this journal offers a streamlined, faster publication ...

  8. Growth in AI and robotics research accelerates

    The number of AI and robotics papers published in the 82 high-quality science journals in the Nature Index (Count) has been rising year-on-year — so rapidly that it resembles an exponential ...

  9. Robotics

    Preview Preview abstract In this paper, we focus on inferring whether the given user command is clear, ambiguous, or infeasible in the context of interactive robotic agents utilizing large language models (LLMs). To tackle this problem, we first present an uncertainty estimation method for LLMs to classify whether the command is certain (i.e., clear) or not (i.e., ambiguous or infeasible).

  10. [2312.07843] Foundation Models in Robotics: Applications, Challenges

    However, significant open research challenges remain, particularly around the scarcity of robot-relevant training data, safety guarantees and uncertainty quantification, and real-time execution. In this survey, we study recent papers that have used or built foundation models to solve robotics problems.

  11. Human-Robot Interaction: Status and Challenges

    Human-robot interaction (HRI) is a rapidly expanding field with a great need for human factors involvement in research and design, especially as robots are challenged to undertake more sophisticated tasks. In any case, the first 90% of replacing humans with robots is much easier than the last 10%. •.

  12. T-RO

    The IEEE Transactions on Robotics (T-RO) publishes research papers that represent major advances in the state-of-the-art in all areas of robotics. The Transactions welcomes original papers that report on any combination of theory, design, experimental studies, analysis, algorithms, and integration and application case studies involving all aspects of robotics.

  13. (PDF) Advanced Applications of Industrial Robotics: New ...

    tific papers were published in 2019 using the term "Industrial robot" as a keyword and, in 2020, the number of papers with a similar interest and research direction incr eased to 5300.

  14. Swarm Robotics: Past, Present, and Future [Point of View]

    Swarm robotics deals with the design, construction, and deployment of large groups of robots that coordinate and cooperatively solve a problem or perform a task. It takes inspiration from natural self-organizing systems, such as social insects, fish schools, or bird flocks, characterized by emergent collective behavior based on simple local interaction rules [1], [2]. Typically, swarm robotics ...

  15. 500 research papers and projects in robotics

    These free, downloadable research papers can shed lights into the some of the complex areas in robotics such as navigation, motion planning, robotic interactions, obstacle avoidance, actuators, machine learning, computer vision, artificial intelligence, collaborative robotics, nano robotics, social robotics, cloud, swan robotics, sensors ...

  16. Robots in Healthcare: a Scoping Review

    Summary. This review found that robots have played 10 main roles across a variety of clinical environments. The two predominant roles were surgical and rehabilitation and mobility. Although robots were mainly studied in the surgical theatre and rehabilitation unit, other settings ranged from the hospital ward to inpatient pharmacy.

  17. [2104.09025] The MIT Humanoid Robot: Design, Motion Planning, and

    View PDF Abstract: Demonstrating acrobatic behavior of a humanoid robot such as flips and spinning jumps requires systematic approaches across hardware design, motion planning, and control. In this paper, we present a new humanoid robot design, an actuator-aware kino-dynamic motion planner, and a landing controller as part of a practical system design for highly dynamic motion control of the ...

  18. (PDF) The future of Robotics Technology

    Abstract. In the last decade the robotics industry has created millions of additional jobs led by consumer electronics and the electric vehicle industry, and by 2020, robotics will be a $100 ...

  19. Trends and research foci of robotics-based STEM ...

    The purpose of this study was to fill a gap in the current review of research on Robotics-based STEM (R-STEM) education by systematically reviewing existing research in this area. This systematic review examined the role of robotics and research trends in STEM education. A total of 39 articles published between 2012 and 2021 were analyzed.

  20. Soft Robotics: A Systematic Review and Bibliometric Analysis

    The second significant research area is "Robotics", with 1080 articles representing 29.340% of the 3681 results. A total of 650 papers that contributed to the field of soft robotics were from the "Nanoscience Nanotechnology" category. ... In this paper, the field of soft robotics has been analyzed from both quantitative and qualitative ...

  21. Google DeepMind's Chatbot-Powered Robot Is Part of a Bigger Revolution

    Robotics researchers are exploring how large language models can give physical machines more smarts. ... In a new paper outlining the project, the researchers behind the work say that their robot ...

  22. The Effects of Robots on the Workplace

    This paper examines the effects of robots across various occupations in US manufacturing plants, extending extant research conducted at the firm and industry levels. We use a difference-in-differences approach matched on industry, commuting zone, and plant size to estimate how employment and skill demand for various occupations change after robot adoption. We find that the introduction of ...

  23. A review of mobile robots: Concepts, methods, theoretical framework

    This article deals with mobile robots and how a mobile robot can move in a real world to fulfill its objectives without human interaction. To understand the basis, it must be noted that in a mobile robot, several technological areas and fields must be observed and integrated for the correct operation of the robot: the locomotion system and kinematics, perception system (sensors), localization ...

  24. Are We Ready to Investigate Robots? Issues and Challenges ...

    In essence, as society marches into a future dominated by robots, this research underscores the need not only for tools and methodologies to investigate them but also for robust design principles to ensure their secure operation. ... concentrated on ransomware attacks on the industrial robot. The study paper evaluates the life cycle of the ...

  25. Google says Gemini AI is making its robots smarter

    The DeepMind robotics team explained in a new research paper how using Gemini 1.5 Pro's long context window — which dictates how much information an AI model can process — allows users to ...

  26. MIT researchers introduce generative AI for databases

    They are joined on the paper by Matin Ghavami and Alexander Lew, MIT graduate students; Cameron Freer, a research scientist; Ulrich Schaechtle and Zane Shelby of Digital Garage; Martin Rinard, an MIT professor in the Department of Electrical Engineering and Computer Science and member of the Computer Science and Artificial Intelligence ...

  27. (PDF) SENSORS IN ROBOTICS AND ITS APPLICATIONS

    This research paper provides a comprehensive overview of how sensors have been integral to the development of robotics and their diverse applications. It explores the different types of sensors ...

  28. [2407.07775] Mobility VLA: Multimodal Instruction Navigation with Long

    An elusive goal in navigation research is to build an intelligent agent that can understand multimodal instructions including natural language and image, and perform useful navigation. To achieve this, we study a widely useful category of navigation tasks we call Multimodal Instruction Navigation with demonstration Tours (MINT), in which the environment prior is provided through a previously ...

  29. Designing autonomous robots for use on Mars and closer to home

    Conlon said much of the research with the robot thus far has been in controlled environments, making Sawicki's MDRS mission a unique deployment opportunity. "We'll be writing a paper from all we've learned from this experience," Sawicki said. "One of the key findings is just how to make the system super robust for a field study ...

  30. NVIDIA Research to present simulation, generative AI advances at

    NVIDIA Research kick-starts developments in physics-based simulation. Graphics researchers are narrowing the gap between physical objects and their virtual representations with physics-based simulation — a range of techniques to make digital objects and characters move the same way they would in the real world. Several NVIDIA Research papers feature breakthroughs in the field, including ...