Learning to collaborate from simulation for robot-assisted dressing
Open accessAbstract
We investigated the application of haptic feedback control and deep reinforcement learning (DRL) to robot-assisted dressing. Our method uses DRL to simultaneously train human and robot control policies as separate neural networks using physics simulations. In addition, we modeled variations in human impairments relevant to dressing, including unilateral muscle weakness, involuntary arm motion, and limited range of motion. Our approach resulted in control policies that successfully collaborate in a variety of simulated dressing tasks involving a hospital gown and a T-shirt. In addition, our approach resulted in policies trained in simulation that enabled a real PR2 robot to dress the arm of a humanoid robot with a hospital gown. We found that training policies for specific impairments dramatically improved performance; that controller execution speed could be scaled after training to reduce the robot's speed without steep reductions in performance; that curriculum learning could be used to lower applied forces; and that multi-modal sensing, including a simulated capacitive sensor, improved performance.
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Although there have been several attempts, designing a simple and effective dense reward function for cloth folding and dressing tasks is still challenging.
“Although there have been several attempts [1,31,38], designing a simple and effective dense reward function for cloth folding and dressing tasks is still challenging.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [31]
The Joint Reward Function section (p. 3) reports that reusing an earlier dressing reward produced unsafe robot behavior. Refining Policies with Curriculum Learning (p. 4) says competing objectives make reward weights difficult to balance, supporting the challenge for dressing. The paper does not address cloth folding or establish the broader claim about designing a simple, effective dense reward for both tasks. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIn cloth-like deformable object manipulation, policy-gradient methods VPG and TRPO are applied in dressing in simulation.
“In CDO manipulation, policy-gradient methods VPG [1] and TRPO [31,32] are applied in dressing in simulation (Section 6.3.2).”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [31]
The complex deformation of the cloth and its occlusion on the body makes human-posture tracking difficult.
“The complex deformation of the cloth and its occlusion on the body makes human-posture tracking difficult [31,304].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [31]
Simulation becomes an ideal place for developing and testing dressing approaches.
“Hence, simulation becomes an ideal place for developing and testing dressing approaches [31,55,317].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [31]
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Wang, Y., Chen, Z., & Ty, G. (2023) · International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI) · doi:10.1109/ricai60863.2023.10489347
Chen, P., Hung, Y.-C., Horng, J., & Wu, L.-C. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4589-3_17
Dressing Assistance Method for Long-Sleeved Shirts Using End-to-End Motion Prediction
Takase, Y., Onda, K., & Yamazaki, K. (2025) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2025.p0710
Ran, X., Zhang, L., Dong, C., Wang, J., Si, W., & Gu, D. (2025) · International Journal of Artificial Intelligence and Robotics Research · doi:10.1142/s297233532550005x
Investigating Learning Assistance by Demonstration for Robotic Wheelchairs: A Simulation Approach
Schettino, V. B., Santos, M. F. D., & Mercorelli, P. (2025) · Robotics · doi:10.3390/robotics14100136
Gomes, M. F. P., da Silva, C. B., Huayhua, E. A. B., Mota, F. A., Olivi, L. R., & Rohmer, E. (2025) · Brazilian Symposium on Robotics and Workshop on Robotics in Education (SBR/WRE) · doi:10.1109/sbr/wre66973.2025.11249651
Evaluating Digital Twin Visualisations for Safety and Trust in Robot-Assisted Dressing
Cogurcu, Y. E., Bartolomei, M., James, B. A., Law, J., Douthwaite, J. A., Rafiq, Y., Mihaylova, L. S., & Dogramadzi, S. (2026) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3805805
Designing of low-DoF robot system from low-dimensional latent space
Yamasaki, K., & Shibata, T. (2026) · ROBOMECH Journal · doi:10.1186/s40648-026-00336-7
Kolanur, B. B., Khyadad, A., Palthur, R., Tapaskar, R. P., & Nirmala, S. R. (2026) · EPJ Web of Conferences · doi:10.1051/epjconf/202636704003
Assistive Robotics for Patient Care: A Review of Physical Human–Robot Interaction Approaches
Naranjo-Campos, F. J., Victores, J. G., Rivero, J., Alcaide, A., & Balaguer, C. (2026) · Applied Sciences · doi:10.3390/app162110538
- Authors
- Clegg, A., Erickson, Z., Grady, P., Turk, G., Kemp, C. C., & Liu, C. K.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2020-04
- Volume
- 5
- Issue
- 2
- Pages
- 2746-2753
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2996206347
- Cited by
- 62
- References
- 34
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 11 · 2023: 13 · 2022: 11 · 2021: 4 · 2020: 4
- FWCI
- 3.96
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Robotics and Automation Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NSF Graduate Research Fellowship (DGE-1650044)
- NSF (IIS-1514258)
- AWS Cloud Credits for Research
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Keywords
- human-robot collaboration, deep reinforcement learning, physics simulation, muscle weakness, range of motion limitation, humanoid robots, PR2 robot, hospital gowns, t-shirts, curriculum learning, capacitive sensors, multimodal sensing, assistive robotics
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