Assistive Robotics

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Assistive robotics studies how robots and wearable devices can help people with disabilities, older adults, and patients perform tasks and participate more independently. Approaches include prostheses and exoskeletons controlled by biosignals or learned policies, shared-autonomy systems for manipulation and navigation, and social robots for therapy and care. Systems are tested in simulations, laboratory tasks, clinical or home settings, and user studies that measure safety, task success, physical effort, workload, and user experience. A central challenge is balancing useful autonomy with user agency and safety while making assistance both personalized and reliable across people, tasks, and real-world environments.

Latest papers

208 papers27 papers in the last four weeks, down 16% on the four weeks before. 0.2% of all new papers.

SMAT: Staged Multi-Agent Training for Co-Adaptive Exoskeleton Control
Mar 8, 2026Yifei Yuan, Ghaith Androwis, Xianlian ZhouHip Exoskeleton Control Assistive Robotics
Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA · Kessler Foundation, West Orange, NJ, 07052, USA
GuideTWSI: A Diverse Tactile Walking Surface Indicator Dataset from Synthetic and Real-World Images for Blind and Low-Vision Navigation
Mar 7, 2026Hochul Hwang, Soowan Yang, Anh N. H. Nguyen +6Visually Impaired Navigation Assistance Assistive Technology for Blind and Low-Vision People
University of Massachusetts Amherst · Daegu Gyeongbuk Institute of Science and Technology · The University of Texas at Austin +2
A Multi-Layer Sim-to-Real Framework for Gaze-Driven Assistive Neck Exoskeletons
Mar 6, 2026Colin Rubow, Eric Brewer, Ian Bales +2Assistive Robotics Robotic Control
Dept. of Mechanical Engineering, University of Utah, UT 84112, USA. · Robotics Center, University of Utah, UT 84112, USA. · Kahlert School of Computing, University of Utah, UT 84112, USA.
Dual-Agent Multiple-Model Reinforcement Learning for Event-Triggered Human-Robot Co-Adaptation in Decoupled Task Spaces
Mar 6, 2026Yaqi Li, Zhengqi Han, Huifang Liu +1Human-Robot Interaction RL for Robotics
Faculty of Engineering and IT, University of Technology Sydney, Australia · College of Medical information and Artificial intelligence, Shandong First Medical University (Shandong Academy of Medical Science) 6699 Qingdao Road, Jinan, Shandong, China
Glance-Say: Multimodal Human-Robot Collaboration and Intent Recognition via Sticky Glance
Mar 6, 2026Yuzhi Lai, Shenghai Yuan, Peizheng Li +2Robotic Control Human-AI Interaction
University of Tuebingen, Geschwister-Scholl-Platz, 72074 Germany · Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
A Pivot-Based Kirigami Utensil for Hand-Held and Robot-Assisted Feeding
Mar 5, 2026Keone Leao, Grace Brotherson, Iain Mischel +2Assistive Robotics Robotics
Collaborative Robotics Lab (Collab), Dept. of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061.
NEFFY 2.0: A Breathing Companion Robot: User-Centered Design and Findings from a Study with Ukrainian Refugees
Mar 5, 2026Ilona Buchem, Jessica Kazubski, Charly GoerkeHuman-Robot Interaction Social Robotics
Berlin University of Applied Sciences Berlin Germany
Rethinking the Role of Collaborative Robots in Rehabilitation
Mar 5, 2026Vivek Gupte, Shalutha Rajapakshe, Emmanuel SenftHuman-Robot Collaboration Assistive Robotics
Idiap Research Institute & EPFL, Switzerland · Idiap Research Institute, Switzerland
On the Strengths and Weaknesses of Data for Open-set Embodied Assistance
Mar 5, 2026Pradyumna Tambwekar, Andrew Silva, Deepak Gopinath +3Assistive Robotics Multimodal Foundation Models
Distyl AI · Toyota Research Institute
TEGA: A Tactile-Enhanced Grasping Assistant for Assistive Robotics via Sensor Fusion and Closed-Loop Haptic Feedback
Mar 5, 2026Hengxu You, Tianyu Zhou, Fang Xu +2Haptic Feedback for Robot Teleoperation Robotic Grasping
All authors are with the Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL 32611, USA. · All authors are with Nvidia
Soft Semi-active Back Support Device with Adaptive Force Profiles using Variable-elastic Actuation and Weight Feedback
Mar 4, 2026Rohan Khatavkar, The Bach Nguyen, Inseung Kang +2Assistive Robotics Soft Robotics
Arizona State University · Carnegie Mellon University
Learning Therapist Policy from Therapist-Exoskeleton-Patient Interaction
Mar 2, 2026Grayson Snyder, Lorenzo Vianello, Levi Hargrove +2Assistive Robotics Human-Robot Interaction
Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA. · Legs and Walking Lab, Shirley Ryan AbilityLab, Chicago, IL, USA.
Co-designing a Social Robot for Newcomer Children's Cultural and Language Learning
Mar 2, 2026Neil Fernandes, Tehniyat Shahbaz, Emily Davies-Robinson +2Social Robotics Assistive Robotics
University of Waterloo, Waterloo, Ontario, Canada · United for Literacy, Toronto, Ontario, Canada
Interface-Aware Trajectory Reconstruction of Limited Demonstrations for Robot Learning
Feb 26, 2026Demiana R. Barsoum, Mahdieh Nejati Javaremi, Larisa Y. C. Loke +1Learning from Suboptimal Demonstrations Trajectory Inference
Northwestern University & Shirley Ryan AbilityLab, Chicago, Illinois, USA
DigiArm: An Anthropomorphic 3D-Printed Prosthetic Hand with Enhanced Dexterity for Typing Tasks
Feb 26, 2026Dean Zadok, Tom Naamani, Yuval Bar-Ratson +5Robotic Hand Design Assistive Robotics
Department of Computer Science, Technion, Haifa, Israel · Department of Mechanical Engineering, Technion, Haifa, Israel
Therapist-Robot-Patient Physical Interaction is Worth a Thousand Words: Enabling Intuitive Therapist Guidance via Remote Haptic Control
Feb 25, 2026Beatrice Luciani, Alex van den Berg, Matti Lang +2Upper-Limb Exoskeletons Human-Robot Interaction
Department of Cognitive Robotics, Delft University of Technology, Delft, The Netherlands · Department of Rehabilitation Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.
Design and Biomechanical Evaluation of a Lightweight Low-Complexity Soft Bilateral Ankle Exoskeleton
Feb 20, 2026Josée Mallah, Zakii Javed, Zafer Azak +2Assistive Robotics Soft Robotics
Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, United Kingdom · Cambridge University Hospitals NHS Foundation Trust, Cambridge CB22 5LD, United Kingdom
Design and Characterization of a Dual-DOF Soft Shoulder Exosuit with Volume-Optimized Pneumatic Actuator
Feb 20, 2026Rui Chen, Domenico Chiaradia, Daniele Leonardis +1Pneumatic Actuation Upper-Limb Exoskeletons
The authors are with the Institute of Mechanical Intelligence and Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna (SSSA), 56127 Pisa, Italy
Reinforcement-Learning-Based Assistance Reduces Squat Effort with a Modular Hip--Knee Exoskeleton
Feb 19, 2026Neethan Ratnakumar, Mariya Huzaifa Tohfafarosh, Saanya Jauhri +1Upper-Limb Exoskeletons Assistive Robotics
Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA.
Proximal powered knee placement: a case study
Feb 19, 2026Kyle R. Embry, Lorenzo Vianello, Jim Lipsey +8Assistive Robotics
Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, USA · Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA · Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
Robot-Assisted Social Dining as a White Glove Service
Feb 17, 2026Atharva S Kashyap, Ugne Aleksandra Morkute, Patricia Alves-OliveiraAssistive Robotics Human-Robot Interaction
Robotics University of Michigan Ann Arbor, Michigan, USA · Leiden University Leiden, Netherlands
Learning From a Steady Hand: A Weakly Supervised Agent for Robot Assistance under Microscopy
Jan 28, 2026Huanyu Tian, Martin Huber, Lingyun Zeng +7Robotic Manipulation Assistive Robotics
H. Tian, M. Huber, Z. Han, L. Zeng, T. Vercauteren, and C. Bergeles are with the School of Biomedical Engineering & Imaging Sciences, King’s College London, UK. · W. Bennett, G. Silvestri, G. Mendizabal-Ruiz, C. Bergeles, and A. Chavez-Badiola are with Conceivable Life Sciences, New York City, US and London, UK. · A. Chavez-Badiola is with Hope IVF Mexico, Mexico.
CRAFT-E: An Interpretable and Editable Neuro-Symbolic Framework for Affordance-Grounded Active Object Retrieval
Dec 3, 2025Zhou Chen, Joe Lin, Carson Bulgin +1Robotic Affordance Inference Robotic Object Search
CSSE Department, Auburn University, Auburn, 36849, AL, USA.
Koopman Model Predictive Control of An Origami-Inspired Soft Exoskeleton for Knee Rehabilitation
Oct 13, 2025Junxiang Wang, Han Zhang, Zehao Wang +3Robotic Control Model Predictive Control
School of Automation and Intelligent Sensing, Institute of Medical Robotics, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China · Seventh Affiliated Hospital, SunYat-sen University, Shenzhen 510275, China
Uncertainty-Aware Ankle Exoskeleton Control
Aug 28, 2025Fatima Mumtaza Tourk, Bishoy Galoaa, Sanat Shajan +3Uncertainty Quantification Ankle Exoskeleton Control
Department of Mechanical Engineering, Northeastern University, Boston, MA 02115 USA
Towards Biosignals-Free Autonomous Prosthetic Hand Control via Imitation Learning
Jun 10, 2025Kaijie Shi, Wanglong Lu, Hanli Zhao +3Robotic Grasping Assistive Robotics
Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada · College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325000, China · Department of Mechanical and Mechatronics Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
Development of a Low-Cost Prosthetic Hand Using Electromyography and Machine Learning
Nov 23, 2024Mosab Diab, Ashraf Mohammed, Yinlai JiangAssistive Robotics Robotic Hand Design
Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Khartoum, Khartoum, Sudan · Center for Neuroscience and Biomedical Engineering, The University of Electro-Communications, Chofu, Tokyo, Japan
Koopman-driven grip force prediction through EMG sensing
Sep 25, 2024Tomislav Bazina, Ervin Kamenar, Maria Fonoberova +1Assistive Robotics
University of Rijeka, Faculty of Engineering, 51000 Rijeka, Vukovarska 58, Croatia

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SMAT: Staged Multi-Agent Training for Co-Adaptive Exoskeleton Control

Yifei Yuan, Ghaith Androwis, Xianlian Zhou

Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA · Kessler Foundation, West Orange, NJ, 07052, USA

Hip Exoskeleton Control Assistive Robotics Robotic Control Multi-Agent Control Curriculum Learning

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Effective exoskeleton assistance requires co-adaptation: as the device alters joint dynamics, the user reorganizes neuromuscular coordination, creating a non-stationary learning problem. Most learning-based approaches do not explicitly account for the sequential nature of human motor adaptation, leading to training instability and poorly timed assistance. We propose Staged Multi-Agent Training (SMAT), a four-stage curriculum designed to mirror how users naturally acclimate to a wearable device. In SMAT, a musculoskeletal human actor and a bilateral hip exoskeleton actor are trained progressively: the human first learns unassisted gait, then adapts to the added device mass; the exoskeleton subsequently learns a positive assistance pattern against a stabilized human policy, and finally both agents co-adapt with full torque capacity and bidirectional feedback. We implement SMAT in the MyoAssist simulation environment using a 26-muscle lower-limb model and an attached hip exoskeleton. Our musculoskeletal simulations demonstrate that the learned exoskeleton control policy produces an average 10.1% reduction in hip muscle activation relative to the no-assist condition. We validated the learned controller in an offline setting using open-source gait data, then deployed it to a physical hip exoskeleton for treadmill experiments with five subjects. The resulting policy delivers consistent assistance and predominantly positive mechanical power without the need for any explicitly imposed timing shift (mean positive power: 13.6 W at 6 Nm RMS torque to 23.8 W at 9.3 Nm RMS torque, with minimal negative power) consistently across all subjects without subject-specific retraining.