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 [BibTeX] [Marc21]
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.
Projects Idiap
Authors Pignat, E.
Calinon, Sylvain
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  • Pignat_RAS_2017.pdf