Learning robot motor skills with mixed reality

• URL: http://arxiv.org/abs/2203.11324v1
• Date: Mon, 21 Mar 2022 20:25:40 GMT
• Title: Learning robot motor skills with mixed reality
• Authors: Eric Rosen, Sreehari Rammohan, Devesh Jha
• Abstract summary: Mixed Reality (MR) has recently shown great success as an intuitive interface for enabling end-users to teach robots. We propose a learning framework where end-users teach robots a) motion demonstrations, b) task constraints, c) planning representations, and d) object information. We hypothesize that conveying this world knowledge will be intuitive with an MR interface, and that a sample-efficient motor skill learning framework will enable robots to effectively solve complex tasks.
• Score: 0.8121462458089141
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mixed Reality (MR) has recently shown great success as an intuitive interface for enabling end-users to teach robots. Related works have used MR interfaces to communicate robot intents and beliefs to a co-located human, as well as developed algorithms for taking multi-modal human input and learning complex motor behaviors. Even with these successes, enabling end-users to teach robots complex motor tasks still poses a challenge because end-user communication is highly task dependent and world knowledge is highly varied. We propose a learning framework where end-users teach robots a) motion demonstrations, b) task constraints, c) planning representations, and d) object information, all of which are integrated into a single motor skill learning framework based on Dynamic Movement Primitives (DMPs). We hypothesize that conveying this world knowledge will be intuitive with an MR interface, and that a sample-efficient motor skill learning framework which incorporates varied modalities of world knowledge will enable robots to effectively solve complex tasks.

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