A framework for robotic clothing assistance by imitation learning
Abstract
The recent demographic trend across developed nations shows a dramatic increase in the aging population, fallen fertility rates and a shortage of caregivers. Hence, the demand for service robots to assist with dressing which is an essential Activity of Daily Living (ADL) is increasing rapidly. Robotic Clothing Assistance is a challenging task since the robot has to deal with two demanding tasks simultaneously, (a) non-rigid and highly flexible cloth manipulation and (b) safe human–robot interaction while assisting humans whose posture may vary during the task. On the other hand, humans can deal with these tasks rather easily. In this paper, we propose a framework for robotic clothing assistance by imitation learning from a human demonstration to a compliant dual-arm robot. In this framework, we divide the dressing task into three phases, i.e. reaching phase, arm dressing phase, and body dressing phase. We model the arm dressing phase as a global trajectory modification using Dynamic Movement Primitives (DMP), while we model the body dressing phase toward a local trajectory modification applying Bayesian Gaussian Process Latent Variable Model (BGPLVM). We show that the proposed framework developed towards assisting the elderly is generalizable to various people and successfully performs a sleeveless shirt dressing task. We also present participants feedback on public demonstration at the International Robot Exhibition (iREX) 2017. To our knowledge, this is the first work performing a full dressing of a sleeveless shirt on a human subject with a humanoid robot.
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It is hard to test assistive dressing in the real world during the development stage, as mannequins cannot be easily actuated.
“Furthermore, it is hard to test assistive dressing in the real world during the development stage, as mannequins cannot be easily actuated [249].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [249]
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Dynamic movement primitives in robotics: A tutorial survey
Saveriano, M., Abu-Dakka, F. J., Kramberger, A., & Peternel, L. (2023) · The International Journal of Robotics Research · doi:10.1177/02783649231201196 · Cited by 267 · see citing works
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“Joshi et al. (2019) [249] break down the task into three consecutive subtasks and apply DMP for dressing the arm and a Bayesian Gaussian Process Latent Variable Model (BGPLVM) for dressing the body.”
BackgroundMethod
- Authors
- Joshi, R. P., Koganti, N., & Shibata, T.
- Affiliations
- Kyushu Institute of Technology (JP)
- Nara Institute of Science and Technology (JP)
- Corresponding author
- Shibata, T.
- Venue
- Advanced Robotics
- Publisher
- Informa UK Limited
- Published
- 2019-07-09
- Volume
- 33
- Issue
- 22
- Pages
- 1156-1174
- ISSN
- 0169-1864 (ISSN-L)
- 1568-5535
- OpenAlex
- W2961077157
- Cited by
- 50
- References
- 12
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 14 · 2023: 4 · 2022: 12 · 2021: 1 · 2020: 3 · 2019: 2
- FWCI
- 2.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Advanced Robotics (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (16H01749)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- dressing assistance, imitation learning, dual-arm robot, dynamic movement primitives, human-robot interaction, fabric manipulation, activities of daily living, elderly care, service robots, trajectory modification
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