Haptic simulation for robot-assisted dressing
Open accessAbstract
There is a considerable need for assistive dressing among people with disabilities, and robots have the potential to fulfill this need. However, training such a robot would require extensive trials in order to learn the skills of assistive dressing. Such training would be time-consuming and require considerable effort to recruit participants and conduct trials. In addition, for some cases that might cause injury to the person being dressed, it is impractical and unethical to perform such trials. In this work, we focus on a representative dressing task of pulling the sleeve of a hospital gown onto a person's arm. We present a system that learns a haptic classifier for the outcome of the task given few (2-3) real-world trials with one person. Our system first optimizes the parameters of a physics simulator using real-world data. Using the optimized simulator, the system then simulates more haptic sensory data with noise models that account for randomness in the experiment. We then train hidden Markov Models (HMMs) on the simulated haptic data. The trained HMMs can then be used to classify and predict the outcome of the assistive dressing task based on haptic signals measured by a real robot's end effector. This system achieves 92.83% accuracy in classifying the outcome of the robot-assisted dressing task with people not included in simulation optimization. We compare our classifiers to those trained on real-world data. We show that the classifiers from our system can categorize the dressing task outcomes more accurately than classifiers trained on ten times more real data.
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The system needs to detect success and different error states.
“In addition, the system needs to detect success and different error states [298,301,309,315,316,317].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [317]
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 [317]
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- Authors
- Yu, W., Kapusta, A., Tan, J., Kemp, C. C., Turk, G., & Liu, C. K.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- 2017 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 6044-6051
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2738522242
- Cited by
- 41
- References
- 28
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 9 · 2022: 9 · 2021: 2 · 2020: 6 · 2019: 4 · 2018: 4 · 2017: 3
- FWCI
- 7.08
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- SMARTech Repository (Georgia Institute of Technology) (submittedVersion)
- Funders
- National Science Foundation (1514258)
- National Institute on Disability, Independent Living, and Rehabilitation Research (90RE5016–01-00, -01-00)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Engineering › Mechanical Engineering › Teleoperation and Haptic Systems
- Keywords
- assistive robotics, haptic simulation, hidden Markov models, few-shot learning, noise models, people with disabilities, robot training
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