Review of Robotics Technologies and Its Applications

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IEEE Transactions on Robotics (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. 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

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 ]

Honorable Mention

"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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  • Conference proceedings
  • © 2023
  • Robotics Research
  • Aude Billard 0 ,
  • Tamim Asfour 1 ,
  • Oussama Khatib 2

EPFL STI SMT-GE, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland

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Institute for Anthropomatics and Robotic, KIT, Karlsruhe, Germany

Artificial intelligence laboratory, department of computer science, stanford university, stanford, usa.

Presents top class research in Robotics Research

Provides outcome of the 20th International Symposium on Robotics Research

Includes contributions from leading researchers and pioneers from academia, government, and industry in robotics

Part of the book series: Springer Proceedings in Advanced Robotics (SPAR, volume 27)

Conference series link(s): ISRR: The International Symposium of Robotics Research

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Conference proceedings info: ISRR 2022.

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Table of contents (37 papers)

Front matter, robot learning, it’s just semantics: how to get robots to understand the world the way we do.

  • Jen Jen Chung, Julian Förster, Paula Wulkop, Lionel Ott, Nicholas Lawrance, Roland Siegwart

Learning Agile, Vision-Based Drone Flight: From Simulation to Reality

  • Davide Scaramuzza, Elia Kaufmann

Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning

  • Niclas Vödisch, Daniele Cattaneo, Wolfram Burgard, Abhinav Valada

Efficiently Learning Single-Arm Fling Motions to Smooth Garments

  • Lawrence Yunliang Chen, Huang Huang, Ellen Novoseller, Daniel Seita, Jeffrey Ichnowski, Michael Laskey et al.

Learning Long-Horizon Robot Exploration Strategies for Multi-object Search in Continuous Action Spaces

  • Fabian Schmalstieg, Daniel Honerkamp, Tim Welschehold, Abhinav Valada

Visual Foresight with a Local Dynamics Model

  • Colin Kohler, Robert Platt

Robot Vision

Monocular camera and single-beam sonar-based underwater collision-free navigation with domain randomization.

  • Pengzhi Yang, Haowen Liu, Monika Roznere, Alberto Quattrini Li

Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints

  • Moshe Shienman, Vadim Indelman

SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding

  • Hermann Blum, Marcus G. Müller, Abel Gawel, Roland Siegwart, Cesar Cadena

6N-DoF Pose Tracking for Tensegrity Robots

  • Shiyang Lu, William R. Johnson III, Kun Wang, Xiaonan Huang, Joran Booth, Rebecca Kramer-Bottiglio et al.

Scale-Invariant Fast Functional Registration

  • Muchen Sun, Allison Pinosky, Ian Abraham, Todd Murphey

Towards Mapping of Underwater Structures by a Team of Autonomous Underwater Vehicles

  • Marios Xanthidis, Bharat Joshi, Monika Roznere, Weihan Wang, Nathaniel Burgdorfer, Alberto Quattrini Li et al.

Grasping and Manipulation

Contact-implicit planning and control for non-prehensile manipulation using state-triggered constraints.

  • Maozhen Wang, Aykut Özgün Önol, Philip Long, Taşkın Padır

Mechanical Search on Shelves with Efficient Stacking and Destacking of Objects

  • Huang Huang, Letian Fu, Michael Danielczuk, Chung Min Kim, Zachary Tam, Jeffrey Ichnowski et al.

Multi-object Grasping in the Plane

  • Wisdom C. Agboh, Jeffrey Ichnowski, Ken Goldberg, Mehmet R. Dogar

Parameter Estimation for Deformable Objects in Robotic Manipulation Tasks

  • David Millard, James A. Preiss, Jernej Barbič, Gaurav S. Sukhatme

Other Volumes

The proceedings of the 2022 edition of the International Symposium of Robotics Research (ISRR) offer a series of peer-reviewed chapters that report on the most recent research results in robotics, in a variety of domains of robotics including robot design, control, robot vision, robot learning, planning, and integrated robot systems. The proceedings entail also invited contributions that offer provocative new ideas, open-ended themes, and new directions for robotics, written by some of the most renown international researchers in robotics.

  • Robotics Future
  • ISRR 2022 proceedings
  • International Symposium on Robotics Research
  • Advanced Robotics

Aude Billard

Tamim Asfour

Oussama Khatib

Book Title : Robotics Research

Editors : Aude Billard, Tamim Asfour, Oussama Khatib

Series Title : Springer Proceedings in Advanced Robotics

DOI : https://doi.org/10.1007/978-3-031-25555-7

Publisher : Springer Cham

eBook Packages : Intelligent Technologies and Robotics , Intelligent Technologies and Robotics (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

Hardcover ISBN : 978-3-031-25554-0 Published: 08 March 2023

Softcover ISBN : 978-3-031-25557-1 Due: 22 March 2024

eBook ISBN : 978-3-031-25555-7 Published: 07 March 2023

Series ISSN : 2511-1256

Series E-ISSN : 2511-1264

Edition Number : 1

Number of Pages : XV, 575

Number of Illustrations : 14 b/w illustrations, 232 illustrations in colour

Topics : Control, Robotics, Mechatronics , Robotics , Computational Intelligence

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best research paper on robotics

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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
  • An Automated Sensing System for Steel Bridge Inspection Using GMR Sensor Array and Magnetic Wheels of Climbing Robot
  • Sensors for Next-Generation Robotics
  • Multi-Robot Sensor Relocation To Enhance Connectivity In A WSN
  • Automated Irrigation System Using Robotics and Sensors
  • 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

  • Integrative Social Robotics Hands-On
  • ProCRob Architecture for Personalized Social Robotics
  • Definitions and Metrics for Social Robotics, along with some Experience Gained in this Domain
  • Transmedia Choreography: Integrating Multimodal Video Annotation in the Creative Process of a Social Robotics Performance Piece
  • Co-designing with children: An approach to social robot design
  • Toward Social Cognition in Robotics: Extracting and Internalizing Meaning from Perception
  • Human Centered Robotics : Designing Valuable Experiences for Social Robots
  • 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

  • Compliance Control and Human-Robot Interaction – International Journal of Humanoid Robotics
  • The Design of Humanoid Robot Using C# Interface on Bluetooth Communication
  • An Integrated System to approach the Programming of Humanoid Robotics
  • Humanoid Robot Slope Gait Planning Based on Zero Moment Point Principle
  • 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

  • CR3A: Cloud Robotics Algorithms Allocation Analysis
  • Cloud Computing and Robotics for Disaster Management
  • ABHIKAHA: Aerial Collision Avoidance in Quadcopter using Cloud Robotics
  • The Evolution Of Cloud Robotics: A Survey
  • Sliding Autonomy in Cloud Robotics Services for Smart City Applications
  • CORE: A Cloud-based Object Recognition Engine for Robotics
  • 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

  • Evolution of Task Partitioning in Swarm Robotics
  • GESwarm: Grammatical Evolution for the Automatic Synthesis of Collective Behaviors in Swarm Robotics
  • A Concise Chronological Reassess Of Different Swarm Intelligence Methods With Multi Robotics Approach
  • The Swarm/Potential Model: Modeling Robotics Swarms with Measure-valued Recursions Associated to Random Finite Sets
  • The TAM: ABSTRACTing complex tasks in swarm robotics research
  • 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

  • Soft Robotics: The Next Generation of Intelligent Machines
  • Soft Robotics: Transferring Theory to Application,” Soft Components for Soft Robots”
  • Advances in Soft Computing, Intelligent Robotics and Control
  • The BRICS Component Model: A Model-Based Development Paradigm For ComplexRobotics Software Systems
  • Soft Mechatronics for Human-Friendly Robotics
  • Seminar Soft-Robotics
  • Special Issue on Open Source Software-Supported Robotics Research.
  • 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

  • Fundamental Theories and Practice in Service Robotics
  • Natural Language Processing in Domestic Service Robotics
  • Localization and Mapping for Service Robotics Applications
  • Designing of Service Robot for Home Automation-Implementation
  • Benchmarking Speech Understanding in Service Robotics
  • The Cognitive Service Robotics Apartment
  • Planning with Task-oriented Knowledge Acquisition for A Service Robot
  • Cognitive Robotics
  • Meta-Morphogenesis theory as background to Cognitive Robotics and Developmental Cognitive Science
  • Experience-based Learning for Bayesian Cognitive Robotics
  • Weakly supervised strategies for natural object recognition in robotics
  • Robotics-Derived Requirements for the Internet of Things in the 5G Context
  • A Comparison of Modern Synthetic Character Design and Cognitive Robotics Architecture with the Human Nervous System
  • PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains
  • The Role of Intention in Cognitive Robotics
  • On Cognitive Learning Methodologies for Cognitive Robotics
  • Relational Enhancement: A Framework for Evaluating and Designing Human-RobotRelationships
  • A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering
  • Spatial Cognition in Robotics
  • IOT Based Gesture Movement Recognize Robot
  • Deliberative Systems for Autonomous Robotics: A Brief Comparison Between Action-oriented and Timelines-based Approaches
  • Formal Modeling and Verification of Dynamic Reconfiguration of Autonomous RoboticsSystems
  • Robotics on its feet: Autonomous Climbing Robots
  • Implementation of Autonomous Metal Detection Robot with Image and Message Transmission using Cell Phone
  • Toward autonomous architecture: The convergence of digital design, robotics, and the built environment
  • Advances in Robotics Automation
  • Data-centered Dependencies and Opportunities for Robotics Process Automation in Banking
  • On the Combination of Gamification and Crowd Computation in Industrial Automation and Robotics Applications
  • Advances in RoboticsAutomation
  • Meshworm With Segment-Bending Anchoring for Colonoscopy. IEEE ROBOTICS AND AUTOMATION LETTERS. 2 (3) pp: 1718-1724.
  • Recent Advances in Robotics and Automation
  • Key Elements Towards Automation and Robotics in Industrialised Building System (IBS)
  • Knowledge Building, Innovation Networks, and Robotics in Math Education
  • The potential of a robotics summer course On Engineering Education
  • Robotics as an Educational Tool: Impact of Lego Mindstorms
  • Effective Planning Strategy in Robotics Education: An Embodied Approach
  • An innovative approach to School-Work turnover programme with Educational Robotics
  • The importance of educational robotics as a precursor of Computational Thinking in early childhood education
  • Pedagogical Robotics A way to Experiment and Innovate in Educational Teaching in Morocco
  • Learning by Making and Early School Leaving: an Experience with Educational Robotics
  • Robotics and Coding: Fostering Student Engagement
  • Computational Thinking with Educational Robotics
  • New Trends In Education Of Robotics
  • Educational robotics as an instrument of formation: a public elementary school case study
  • Developmental Situation and Strategy for Engineering Robot Education in China University
  • Towards the Humanoid Robot Butler
  • YAGI-An Easy and Light-Weighted Action-Programming Language for Education and Research in Artificial Intelligence and Robotics
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  • The importance and purpose of simulation in robotics
  • An Educational Tool to Support Introductory Robotics Courses
  • Lollybot: Where Candy, Gaming, and Educational Robotics Collide
  • Assessing the Impact of an Autonomous Robotics Competition for STEM Education
  • Educational robotics for promoting 21st century skills
  • New Era for Educational Robotics: Replacing Teachers with a Robotic System to Teach Alphabet Writing
  • Robotics as a Learning Tool for Educational Transformation
  • The Herd of Educational Robotic Devices (HERD): Promoting Cooperation in RoboticsEducation
  • Robotics in physics education: fostering graphing abilities in kinematics
  • Enabling Rapid Prototyping in K-12 Engineering Education with BotSpeak, a UniversalRobotics Programming Language
  • Innovating in robotics education with Gazebo simulator and JdeRobot framework
  • How to Support Students’ Computational Thinking Skills in Educational Robotics Activities
  • Educational Robotics At Lower Secondary School
  • Evaluating the impact of robotics in education on pupils’ skills and attitudes
  • Imagining, Playing, and Coding with KIBO: Using Robotics to Foster Computational Thinking in Young Children
  • How Does a First LEGO League Robotics Program Provide Opportunities for Teaching Children 21st Century Skills
  • A Software-Based Robotic Vision Simulator For Use In Teaching Introductory Robotics Courses
  • Robotics Practical
  • A project-based strategy for teaching robotics using NI’s embedded-FPGA platform
  • Teaching a Core CS Concept through Robotics
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  • DRAFT: for Automaatiop iv t22 MOKASIT: Multi Camera System for Robotics Monitoring and Teaching
  • MOKASIT: Multi Camera System for Robotics Monitoring and Teaching
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  • Sumo Robot Competition
  • Engagement of students with Robotics-Competitions-like projects in a PBL Bsc Engineering course
  • Robo Camp K12 Inclusive Outreach Program: A three-step model of Effective Introducing Middle School Students to Computer Programming and Robotics
  • The Effectiveness of Robotics Competitions on Students’ Learning of Computer Science
  • Engaging with Mathematics: How mathematical art, robotics and other activities are used to engage students with university mathematics and promote
  • Design Elements of a Mobile Robotics Course Based on Student Feedback
  • Sixth-Grade Students’ Motivation and Development of Proportional Reasoning Skills While Completing Robotics Challenges
  • Student Learning of Computational Thinking in A Robotics Curriculum: Transferrable Skills and Relevant Factors
  • A Robotics-Focused Instructional Framework for Design-Based Research in Middle School Classrooms
  • Transforming a Middle and High School Robotics Curriculum
  • Geometric Algebra for Applications in Cybernetics: Image Processing, Neural Networks, Robotics and Integral Transforms
  • Experimenting and validating didactical activities in the third year of primary school enhanced by robotics technology

Construction

  • Bibliometric analysis on the status quo of robotics in construction
  • AtomMap: A Probabilistic Amorphous 3D Map Representation for Robotics and Surface Reconstruction
  • Robotic Design and Construction Culture: Ethnography in Osaka University’s Miyazaki Robotics Lab
  • Infrastructure Robotics: A Technology Enabler for Lunar In-Situ Resource Utilization, Habitat Construction and Maintenance
  • A Planar Robot Design And Construction With Maple
  • Robotics and Automations in Construction: Advanced Construction and FutureTechnology
  • Why robotics in mining
  • Examining Influences on the Evolution of Design Ideas in a First-Year Robotics Project
  • 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
  • Auotomatic Pick And Place Robot
  • Video Prompting to Teach Robotics and Coding to Students with Autism Spectrum Disorder
  • Bilateral Anesthesia Mumps After RobotAssisted Hysterectomy Under General Anesthesia: Two Case Reports
  • Future Prospects of Artificial Intelligence in Robotics Software, A healthcare Perspective
  • Designing new mechanism in surgical robotics
  • Open-Source Research Platforms and System Integration in Modern Surgical Robotics
  • Soft Tissue Robotics–The Next Generation
  • CORVUS Full-Body Surgical Robotics Research Platform
  • OP: Sense, a rapid prototyping research platform for surgical robotics
  • Preoperative Planning Simulator with Haptic Feedback for Raven-II Surgical Robotics Platform
  • Origins of Surgical Robotics: From Space to the Operating Room
  • Accelerometer Based Wireless Gesture Controlled Robot for Medical Assistance using Arduino Lilypad
  • The preliminary results of a force feedback control for Sensorized Medical Robotics
  • Medical robotics Regulatory, ethical, and legal considerations for increasing levels of autonomy
  • Robotics in General Surgery
  • Evolution Of Minimally Invasive Surgery: Conventional Laparoscopy Torobotics
  • Robust trocar detection and localization during robot-assisted endoscopic surgery
  • How can we improve the Training of Laparoscopic Surgery thanks to the Knowledge in Robotics
  • Discussion on robot-assisted laparoscopic cystectomy and Ileal neobladder surgery preoperative care
  • Robotics in Neurosurgery: Evolution, Current Challenges, and Compromises
  • Hybrid Rendering Architecture for Realtime and Photorealistic Simulation of Robot-Assisted Surgery
  • Robotics, Image Guidance, and Computer-Assisted Surgery in Otology/Neurotology
  • Neuro-robotics model of visual delusions
  • Neuro-Robotics
  • Robotics in the Rehabilitation of Neurological Conditions
  • What if a Robot Could Help Me Care for My Parents
  • A Robot to Provide Support in Stigmatizing Patient-Caregiver Relationships
  • A New Skeleton Model and the Motion Rhythm Analysis for Human Shoulder Complex Oriented to Rehabilitation Robotics
  • Towards Rehabilitation Robotics: Off-The-Shelf BCI Control of Anthropomorphic Robotic Arms
  • Rehabilitation Robotics 2013
  • Combined Estimation of Friction and Patient Activity in Rehabilitation Robotics
  • Brain, Mind and Body: Motion Behaviour Planning, Learning and Control in view of Rehabilitation and Robotics
  • Reliable Robotics – Diagnostics
  • Robotics for Successful Ageing
  • Upper Extremity Robotics Exoskeleton: Application, Structure And Actuation

Defence and Military

  • Voice Guided Military Robot for Defence Application
  • Design and Control of Defense Robot Based On Virtual Reality
  • AI, Robotics and Cyber: How Much will They Change Warfare
  • BORDER SECURITY ROBOT
  • Brain Controlled Robot for Indian Armed Force
  • Autonomous Military Robotics
  • Wireless Restrained Military Discoursed Robot
  • Bomb Detection And Defusion In Planes By Application Of Robotics
  • Impacts Of The Robotics Age On Naval Force Design, Effectiveness, And Acquisition

Space Robotics

  • Lego robotics teacher professional learning
  • New Planar Air-bearing Microgravity Simulator for Verification of Space Robotics Numerical Simulations and Control Algorithms
  • The Artemis Rover as an Example for Model Based Engineering in Space Robotics
  • Rearrangement planning using object-centric and robot-centric action spaces
  • Model-based Apprenticeship Learning for Robotics in High-dimensional Spaces
  • Emergent Roles, Collaboration and Computational Thinking in the Multi-Dimensional Problem Space of Robotics
  • Reaction Null Space of a multibody system with applications in robotics

Other Industries

  • Robotics in clothes manufacture
  • Recent Trends in Robotics and Computer Integrated Manufacturing: An Overview
  • Application Of Robotics In Dairy And Food Industries: A Review
  • Architecture for theatre robotics
  • Human-multi-robot team collaboration for efficent warehouse operation
  • 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

  • The future of Robotics Technology
  • RoboticsAutomation Are Killing Jobs A Roadmap for the Future is Needed
  • The next big thing (s) in robotics
  • Robotics in Indian Industry-Future Trends
  • The Future of Robot Rescue Simulation Workshop
  • PreprintQuantum Robotics: Primer on Current Science and Future Perspectives
  • Emergent Trends in Robotics and Intelligent Systems

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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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Object name is micromachines-14-00359-g001.jpg

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.

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.

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.

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.

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.

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.

Analysis of bibliographic references according to the materials.

Analysis of bibliographic references according to the actuators.

Analysis of references according to the specific technologies.

Analysis of references according to the modelling methods.

Analysis of bibliographic references according to the sensors.

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.

Colloidal robotics

Affiliations.

  • 1 Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • 2 Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
  • 3 Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • 4 Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA.
  • 5 School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.
  • 6 Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
  • 7 School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.
  • 8 School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA.
  • 9 Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. [email protected].
  • 10 Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. [email protected].
  • PMID: 37620646
  • DOI: 10.1038/s41563-023-01589-y

Robots have components that work together to accomplish a task. Colloids are particles, usually less than 100 µm, that are small enough that they do not settle out of solution. Colloidal robots are particles capable of functions such as sensing, computation, communication, locomotion and energy management that are all controlled by the particle itself. Their design and synthesis is an emerging area of interdisciplinary research drawing from materials science, colloid science, self-assembly, robophysics and control theory. Many colloidal robot systems approach synthetic versions of biological cells in autonomy and may find ultimate utility in bringing these specialized functions to previously inaccessible locations. This Perspective examines the emerging literature and highlights certain design principles and strategies towards the realization of colloidal robots.

© 2023. Springer Nature Limited.

Publication types

Grants and funding.

  • FA9550-15-1-0514/United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (AF Office of Scientific Research)
  • W911NF-19-1-0233/United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office (ARO)
  • W911NF-19-10372/United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office (ARO)

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Building robots to get kids hooked on STEM subjects

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How to Save Humanity in 17 Goals: Quality education and lifelong learning for all (SDG 4)

As a child Solomon King Benge loved Eric Laithwaite’s 1974 book The Engineer in Wonderland , based on the mechanical engineer’s 1966 Royal Institution Christmas lectures. After reading it he asked his physics teacher if he and his classmates might try some of Laithwaite’s practical experiments, but was told: “Don’t waste your time with this. This is not important, because it’s not in the curriculum.”

The rejection promoted Benge to launch Fundi Bots in 2011. The social education initiative aims to give education a stronger practical focus, a move away from learning by rote in front of a blackboard. Last year it reached 22,000 students, most of them in Uganda, and hopes eventually to cover one million across Africa.

Robotics is a key component of the program. Benge recalls one child in northern Uganda who built a sensor-driven robot and was asked what he might do with it. He said: “I think I can now create something that lets the goats out of the pen in the morning so that I don’t have to wake up early.”

Benge tells the How to save humanity in 17 goals podcast series: “It was hilarious for us, but a very real testament of once you empower children and make learning meaningful, then they actually begin looking at the practical applications of that learning.”

The educator and entrepreneur describes how Fundi Bots addresses SDG 4 and its aim to deliver quality education and lifelong learning for all by 2030.

Each episode in the series features researchers whose work addresses one or more the targets. The first six episodes are produced in partnership with Nature Food, and introduced by Juliana Gil, its chief editor.

doi: https://doi.org/10.1038/d41586-024-00211-8

Supported content

This Working Scientist podcast series is sponsored by the University of Queensland, where research is addressing some of the world’s most challenging and complex problems. Take your research further at UQ. Visit uq.edu.au

Sponsor message: 00:00

This Working Scientist podcast series is sponsored by the University of Queensland, where research is addressing some of the world’s most challenging and complex problems.

Take your research further at UQ. Visit uq.edu.au

Juliana Gil: 00:25

Hello, this is How to Save Humanity 17 Goals, a podcast brought to you by Nature Careers in partnership with Nature Food . I am Juliana Gil, chief editor at Nature Food.

Welcome again to the series where we meet the scientists working towards the Sustainable Development Goals, agreed by the United Nations and world leaders in 2015.

Since then, in a huge global effort, thousands of researchers have been tackling the biggest problems that the planet faces today.

In episode four, we look at Sustainable Development Goal number four: how to ensure quality education for all.

And we meet an engineer from Uganda who is changing the way children learn science right across the African continent.

Solomon King Benge: 01:15

My name is Solomon King Benge. And I’m the founder and executive director of Fundi Bots. So Fundi Bots is an organization based in Uganda that is working to improve and accelerate science learning in Africa. We focus very, very heavily on science subjects.

And the goal for our work basically is to move the quality of education from theory-driven blackboard-centred learning to highly practical student-centred learning, in which the pedagogy revolves around understanding the practice as opposed to academic excellence, which typically leads to rote memorization and all that.

So we use multiple tools. The one that we’re most known for is the robotics tool, where we teach children in primary school and secondary school, and some university students, how to work with robots.

And the goal is that the journey of building a robot is a journey of discovery that is exciting. Once a child sees a demo robot, they’re so excited to get it working. So they sort of, like, give us permission to teach them. So I like to call it permission-driven education.

The other tool that we have is a little more aligned to the curriculum. So it has a more academic bent in that it is designed to integrate directly in the national curriculum.

And the reason for this is when we were analyzing the results of our work, the big question that came to us was, “How do we create more impactful learning where the problem centre is?” And the problem centre is typically within the classroom? And that is, what resources do teachers have to teach science well? And what resources do students have to understand the content?

So we build something that we call the enhanced science curriculum. And the goal for that is to integrate directly into the national curriculum almost word for word, but provide high quality tools that both students and teachers use in the classroom to transform the classroom from a blackboard-centred activity to students working in groups, sharing their findings and making exciting discoveries about science.

Solomon King Benge: 03:31

Sustainable development goal number four is ensuring quality education. And the goal is to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.

So the advantage that we have is that a lot of the Sustainable Development Goals are general quality of life ambitions that any country or the world should have.

The categorization is helpful, but it is something that we are inherently working on. So the goal has quite a few targets. And almost all are very aligned to the work that we do. So ensuring that girls and boys have equal and free education, ensuring access to quality, technical and vocational education, early childhood development etc. technical skills, vocational skills, all of those are very, very highly aligned to what we are doing. So we are working towards it. But mostly because of the necessity that we have.

Our long term goal is to work with more than one million students across Africa. Currently, we are primarily based in Uganda. We have done trainings in Tanzania, we have done trainings in Kenya, and we've done some trainings in in Rwanda as well.

But our goal is essentially to replicate all this effort across the African continent. So the story of Fundi Bots, the journey of Fundi Bots, is, I like to tell people that I am essentially reaching back in time to try and redeem myself.

I was the kind of kid that you find in a neighborhood tinkering, tinkering with, like, electronics parts, like trying to understand what made this thing stick. Like, a radio is dead. But why is it dead? I grew up in the 90s. And it was rife with a lot more accessible electronics. So a lot of electronics these days, it’s like, very embedded, it’s very hard to get parts from it. But back in the day, you'd open up a radio, and you find electric motors, you find wires, you find all these things that for a curious child was just like heaven.

And so I was that child, I was essentially trying to understand how things work, putting things together, making toys that were very unlike the kind of toys that my fellow kids were aware were making. Because mine were driven by electricity.

And the the frustration that I felt was even more in the academic setting, because in school it was just about memorizing information so that you could pass an exam. And I found that pretty frustrating, because even at that age, I still felt like there had to be something a little bit more to education than just sitting in a classroom and memorizing facts.

When I got to secondary school, I discovered that it was just another higher profile academic setting where everything that you did, even when it was practical, was aligned towards getting the facts you need, so that you can pass an exam.

The moment of inflection for me, that both solidified my desire for an alternate form of learning, but also increased my frustration, was discovering a very amazing book called The Engineer in Wonderland by ER Laithwaite.

And he used to give Christmas lectures at the, I think the Royal Academy of Sciences or the Royal Society. And he wrote a book called Engineer in Wonderland . And I loved to read. So the story of Alice in Wonderland immediately resonated for me.

And it was this very complex book on electricity and magnetism. But he told it in such an approachable way that even a child like me could understand.

And it was just so much fun, and so exciting. And so I got the book, went to my physics teacher and said, “Hey, can we, can we do this? This looks like something that kids would actually enjoy learning?” He took one look at it, and essentially say, “Don’t waste your time with this, this is not important, because it’s not in the curriculum.”

So at that point, subconsciously, and resolutely, as you know, as far as a 14, 13 year old can be resolute, I realized that, you know, this education as it was just wasn’t the thing for me.

But in 2011 is when the Fundi Bot story sort of came back full circle. Because when I got that rejection from the teacher, the first thought that came to mind was, “There has to be something better than this.”

And that’s something for me was a place of learning where kids would not be judged on what was exciting for them. They would not be pressured into, you know, academic environments, but it was a place where knowledge was free, the kids were mentored, etc.

So that sort of stayed with me, lingered at the back of my mind. You know, I basically told myself that this dream that I had, as a child, I think I can start working on it now.

I started Fundbots as a hobby. And then in 2014, it became a full time organization. So what started as a solo, you know, project, suddenly began attracting people. We began working with more and more students, we began attracting a lot of funding.

And right now we are at a stage where we are a team of 125. And last year alone, we trained more than 22,000 students.

Our interventions are in three major areas. One is learning from home, which we call the Fundi At Home program.

The other is learning to prepare for work, which is a more skills development-oriented perspective, which we call Fundi At Work.

And then the big one is school-based, which we call Fundi At School. So each of those provides learning options and learning perspectives for students in different ways.

And so the one million that we want to reach, the majority of them are in schools, the ones that we will reach directly are in schools. But we are also building digital content that children can access through the internet.

So YouTube is a current primary platform, but this year we plan to roll out an online learning system where any kid across Africa can log on (with the help of their parents, of course), any kid across Africa can log on and begin learning the material that we are teaching.

We also want to do broadcast, which essentially means putting our content on TV and syndicating it across the African continent.

So when you look at those very highly scaleable options, they may not be as practical as we would like, but it still allows us to reach a significantly diverse and significantly broad audience.

And the hope is that in every single one of those interventions we will create ways in which kids can learn experientially by trying experiments on their own, but also academically by having a high quality learning perspective in the classroom.

Solomon King Benge 10:54

So our learning models are essentially centered around what kind of access we have to the children. The robotics program tends to happen more on the weekends.

Some schools might give us some classroom time, but typically, they happen on weekends. It’s like an after school program bordering on a club basis. So we do have teachers that go to the schools every single day, and work with the students and other, and other teachers.

So we have a lot of teachers on staff. The vast majority of our staff members are teachers that support other teachers in schools. So they will go to schools. They might have a suitcase full of electronics, or they might be on a DIY project.

And so students are asked to pick up cardboard, some wires, some materials from their neighbourhood. And the goal essentially, is to lead them on a journey where they make these things themselves.

The big challenge with robotics education initiatives is that many of them are from the west and they are very top down. They don’t take into consideration the local perspectives and the local context.

So you'll find a child is being taught robotics using a $300, $400 robot. And their first instinct is, “This is exciting. But I cannot do this because I don't have this kind of money to go and buy something.”

The Fundi Bots model is completely different. We teach kids how to make all sorts of gadgets out of cardboard, wood, plastic wires. When you look at the robots that our kids made, you can tell that that was built by a child and that they know exactly how it works, you know?

And so for us, that is exciting, because we open up a lot more creativity, innovation and ingenuity.

Solomon King Benge 12:39

The vast majority of robots that our students build are what we call rovers, which is essentially a four-wheeled vehicle.

So that’s a machine that has tyres, a couple of wheels. It is controlled by some sort of very rudimentary circuit.

So depending on the age of the child, that rover can get more and more complex, or it can get very, very simple. Sometimes all you need to do to get a kid excited is for them to actually connect a motor and a battery and see their thing move.

And so it stretches the gamut, all the way from something as simple as that to something like a robot that is trying to navigate its way around an environment.

On the other hand, we also have students that build projects like greenhouses that are controlled by smarthome software. We have students build mock traffic lights for the roads in the villages.

One of my most exciting ones was when we taught this kid in northern Uganda how to build a sensor-driven robot. And we asked him “So what do you think you can do with this?”

And his first reaction was “I think I can now create something that lets the goats out of the pen in the morning so that I don’t have to wake up early, right?”

And while it was hilarious for us, it was just a very real testament of once you empower children and make learning meaningful, then they actually begin looking at the practical applications of that learning.

It’s no longer about an exam. It’s about actual real world solutions. In fact, one of the things that we actively encourage is our students to be able to consider a problem in their communities that they can provide a solution for.

One of the ones that gives us tremendous joy is a group of students from Northern Uganda that made a solar-powered cooker that ended up in the news headlines. And they actually won a sustainability award at the recent climate change conference in Dubai.

So none of this would have been possible if we had a rigid structure that was very guided. We like kids to explore. We like them to experiment. And so our robotics program is not 100% robotics in the traditional sense but robotics is a gateway for kids to begin exploring the capabilities of electronics or of computing. So they can go on to explore programming or to explore electrical engineering or mechanical engineering.They don’t have to do robotics.

In order for sustainability goal number four to be achieved, I think the biggest player in all of this is government. We need to have very, very strong intentionality from the highest levels.

You can have as many actors like Fundi Bots, as many individuals, as many organizations trying to change this landscape, but what we are essentially doing is the government’s work. We do not have the capacity, interest or finances to employ hundreds or thousands of teachers.

This is supposed to be government work. we do not have the resources and the infrastructure to provide learning materials for an entire continent.

But the reason we do this is because at the highest level, there is no capacity, no intentionality, or no interest in funding some of these things. And even if there is interest, even if there is intentionality, there is always a breakdown because there's so many factors from a policy perspective.

From the moment a decision is made to the moment of implementation could be years. And in that time, millions of kids have passed through the school system and their lives have been changed. Literally, every single day that passes there's a kid that's dropping out of school who could have benefited from a high quality education.

So these decisions take time. I understand that the time is necessary, but they are extremely costly from a human capital perspective, because these are the kids that we need for tomorrow’s workforce.

So the biggest intentionality has to start from the top. I would say that it pretty much narrows down to the most critical actors are teachers.

We need to put teachers as high priority workforce, you know? Looking at quality of training, quality of compensation, quality of tools and resources that they’re given.

We need to empower teachers to love the work that they’re doing. And we need to, quite honestly, pick the best teachers because many teachers get into the profession because it’s a last resort. So I think that for me, teachers are the biggest catalyst.

And if we train them right, if we filter them right, and if we give them the right resources, then that goal is basically achievable on its own. But there has to be maximum intentionality at the government level.

Solomon King Benge 17:37

I absolutely love my job. The part that I love the most about my work, I no longer do. And that was the tinkering, the training, interacting with the kids. Like I really, really love teaching. Unfortunately, my work now is more about fundraising.

So I spend more time in Excel and Word compared to, like, a lab, and programming, or soldering stuff. But I do love the impact that we're having on the lives of children. I love it when teachers tell us the impact that our work is having on not just the students but on them as well.

So it’s really exciting. It’s very exhausting. It’s very draining sometimes because my work is to fundraise. So looking for the money can be an exhaustive, an exhausting and disappointing process.

But it’s all is about like “We just need to keep grinding because the kids need this.” Like I said, every day that passes there's a child that's that's going out of a system and we have failed that child.

Juliana Gil: 18:40

Thanks for listening to this series how to save humanity seven singles. Join us again next week when we look at Sustainable Development Goal number five: how to achieve gender equality and empower all women and girls.

See you then.

Sponsor message: 19:16

This Working Scientist podcast series is sponsored by the University of Queensland, where researchers addressing some of the world's most challenging and complex problems. Take your research further at UQ. Visit uq.edu.au

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best research paper on robotics

Top 5 Robot Trends 2024

New Technology simplifies Automation

best research paper on robotics

1 – Artificial Intelligence (AI) and machine learning

The trend of using Artificial Intelligence in robotics and automation keeps growing. The emergence of generative AI opens-up new solutions. This subset of AI is specialized to create something new from things it’s learned via training, and has been popularized by tools such as ChatGPT. Robot manufacturers are developing generative AI-driven interfaces which allow users to program robots more intuitively by using natural language instead of code. Workers will no longer need specialized programming skills to select and adjust the robot´s actions.

Another example is predictive AI analyzing robot performance data to identify the future state of equipment. Predictive maintenance can save manufacturers machine downtime costs. In the automotive parts industry, each hour of unplanned downtime is estimated to cost US$1.3m - the Information Technology & Innovation Foundation reports. This indicates the massive cost-saving potential of predictive maintenance. Machine learning algorithms can also analyze data from multiple robots performing the same process for optimization. In general, the more data a machine learning algorithm is given, the better it performs.

2 – Cobots expanding to new applications

Human-robot collaboration continues to be a major trend in robotics. Rapid advances in sensors, vision technologies and smart grippers allow robots to respond in real-time to changes in their environment and thus work safely alongside human workers.

Collaborative robot applications offer a new tool for human workers, relieving and supporting them. They can assist with tasks that require heavy lifting, repetitive motions, or work in dangerous environments.

The range of collaborative applications offered by robot manufacturers continues to expand.

A recent market development is the increase of cobot welding applications, driven by a shortage of skilled welders. This demand shows that automation is not causing a labor shortage but rather offers a means to solve it. Collaborative robots will therefore complement – not replace – investments in traditional industrial robots which operate at much faster speeds and will therefore remain important for improving productivity in response to tight product margins.

New competitors are also entering the market with a specific focus on collaborative robots. Mobile manipulators, the combination of collaborative robot arms and mobile robots (AMRs), offer new use cases that could expand the demand for collaborative robots substantially.

3 – Mobile Manipulators

Mobile manipulators – so called “MoMas” - are automating material handling tasks in industries such as automotive, logistics or aerospace. They combine the mobility of robotic platforms with the dexterity of manipulator arms. This enables them to navigate complex environments and manipulate objects, which is crucial for applications in manufacturing. Equipped with sensors and cameras, these robots perform inspections and carry out maintenance tasks on machinery and equipment. One of the significant advantages of mobile manipulators is their ability to collaborate and support human workers. Shortage of skilled labor and a lack of staff applying for factory jobs is likely to increase demand.

4 – Digital Twins

Digital twin technology is increasingly used as a tool to optimize the performance of a physical system by creating a virtual replica. Since robots are more and more digitally integrated in factories, digital twins can use their real-world operational data to run simulations and predict likely outcomes. Because the twin exists purely as a computer model, it can be stress-tested and modified with no safety implications while saving costs. All experimentation can be checked before the physical world itself is touched. Digital twins bridge the gap between digital and physical worlds.

5 – Humanoid Robots

Robotics is witnessing significant advancements in humanoids, designed to perform a wide range of tasks in various environments. The human-like design with two arms and two legs allows the robot to be used flexibly in work environments that were actually created for humans. It can therefore be easily integrated e.g. into existing warehouse processes and infrastructure.

best research paper on robotics

The Chinese Ministry of Industry and Information Technology (MIIT) recently published detailed goals for the country’s ambitions to mass-produce humanoids by 2025. The MIIT predicts humanoids are likely to become another disruptive technology, similar to computers or smartphones, that could transform the way we produce goods and the way humans live.

The potential impact of humanoids on various sectors makes them an exciting area of development, but their mass market adoption remains a complex challenge. Costs are a key factor and success will depend on their return on investment competing with well-established robot solutions like mobile manipulators, for example.

“The five mutually reinforcing automation trends in 2024 show that robotics is a multidisciplinary field where technologies are converging to create intelligent solutions for a wide range of tasks,” says Marina Bill, President of the International Federation of Robotics. “These advances continue to shape the merging industrial and service robotics sectors and the future of work.”

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Global Robotics Race: Korea, Singapore and Germany in the Lead

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Title: an interactive agent foundation model.

Abstract: The development of artificial intelligence systems is transitioning from creating static, task-specific models to dynamic, agent-based systems capable of performing well in a wide range of applications. We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents across a wide range of domains, datasets, and tasks. Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction, enabling a versatile and adaptable AI framework. We demonstrate the performance of our framework across three separate domains -- Robotics, Gaming AI, and Healthcare. Our model demonstrates its ability to generate meaningful and contextually relevant outputs in each area. The strength of our approach lies in its generality, leveraging a variety of data sources such as robotics sequences, gameplay data, large-scale video datasets, and textual information for effective multimodal and multi-task learning. Our approach provides a promising avenue for developing generalist, action-taking, multimodal systems.

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Article paid for by: Ocasio Media The news and editorial staffs of the Bay Area News Group had no role in this post’s preparation.

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