Learning adaptive dressing assistance from human demonstration
| Type of publication: | Journal paper |
| Citation: | Pignat_RAS_2017 |
| Publication status: | Published |
| Journal: | Robotics and Autonomous Systems |
| Volume: | 93 |
| Year: | 2017 |
| Month: | July |
| Pages: | 61-75 |
| URL: | http://doi.org/10.1016/j.robot... |
| DOI: | 10.1016/j.robot.2017.03.017 |
| Abstract: | For tasks such as dressing assistance, robots should be able to adapt to different user morphologies, preferences and requirements. We propose a programming by demonstration method to efficiently learn and adapt such skills. Our method encodes sensory information (relative to the human user) and motor commands (relative to the robot actuation) as a joint distribution in a hidden semi-Markov model. The parameters of this model are learned from a set of demonstrations performed by a human. Each state of this model represents a sensorimotor pattern, whose sequencing can produce complex behaviors. This method, while remaining lightweight and simple, encodes both time-dependent and independent behaviors. It enables the sequencing of movement primitives in accordance to the current situation and user behavior. The approach is coupled with a task-parametrized model, allowing adaptation to different users’ morphologies, and with a minimal intervention controller, providing safe interaction with the user. We evaluate the approach through several simulated tasks and two different dressing scenarios with a bi-manual Baxter robot. |
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| Projects |
Idiap I-DRESS |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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