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Conference papers

Learning Ability Models for Human-Robot Collaboration

Abstract : Our vision is a pro-active robot that assists elderly or disabled people in everyday activities. Such a robot needs knowledge in the form of prediction models about a person’s abilities, preferences and expectations in order to decide on the best way to assist. We are interested in learning such models from observation. We report on a first approach to learn ability models for manipulation tasks and identify some general challenges for the acquisition of human models.
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Contributor : Alexandra Kirsch Connect in order to contact the contributor
Submitted on : Wednesday, November 30, 2016 - 1:39:08 PM
Last modification on : Thursday, January 6, 2022 - 11:38:04 AM
Long-term archiving on: : Monday, March 27, 2017 - 9:32:23 AM

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  • HAL Id : hal-01405755, version 1

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Alexandra Kirsch, Fan Cheng. Learning Ability Models for Human-Robot Collaboration. Robotics: Science and Systems (RSS) --- Workshop on Learning for Human-Robot Interaction Modeling, 2010, Zaragoza, Spain. ⟨hal-01405755⟩

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