Robust Robot Planning for Human-Robot Collaboration

• URL: http://arxiv.org/abs/2302.13916v1
• Date: Mon, 27 Feb 2023 16:02:48 GMT
• Title: Robust Robot Planning for Human-Robot Collaboration
• Authors: Yang You, Vincent Thomas, Francis Colas, Rachid Alami, Olivier Buffet
• Abstract summary: In human-robot collaboration, the objectives of the human are often unknown to the robot. We propose an approach to automatically generate an uncertain human behavior (a policy) for each given objective function. We also propose a robot planning algorithm that is robust to the above-mentioned uncertainties.
• Score: 11.609195090422514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In human-robot collaboration, the objectives of the human are often unknown to the robot. Moreover, even assuming a known objective, the human behavior is also uncertain. In order to plan a robust robot behavior, a key preliminary question is then: How to derive realistic human behaviors given a known objective? A major issue is that such a human behavior should itself account for the robot behavior, otherwise collaboration cannot happen. In this paper, we rely on Markov decision models, representing the uncertainty over the human objective as a probability distribution over a finite set of objective functions (inducing a distribution over human behaviors). Based on this, we propose two contributions: 1) an approach to automatically generate an uncertain human behavior (a policy) for each given objective function while accounting for possible robot behaviors; and 2) a robot planning algorithm that is robust to the above-mentioned uncertainties and relies on solving a partially observable Markov decision process (POMDP) obtained by reasoning on a distribution over human behaviors. A co-working scenario allows conducting experiments and presenting qualitative and quantitative results to evaluate our approach.

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