Towards Robot Task Planning From Probabilistic Models of Human Skills release_gjjv4z6ybjg2dcknt3bqcyzkxq

by Chris Paxton, Marin Kobilarov, Gregory D. Hager

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2016  

Abstract

We describe an algorithm for motion planning based on expert demonstrations of a skill. In order to teach robots to perform complex object manipulation tasks that can generalize robustly to new environments, we must (1) learn a representation of the effects of a task and (2) find an optimal trajectory that will reproduce these effects in a new environment. We represent robot skills in terms of a probability distribution over features learned from multiple expert demonstrations. When utilizing a skill in a new environment, we compute feature expectations over trajectory samples in order to stochastically optimize the likelihood of a trajectory in the new environment. The purpose of this method is to enable execution of complex tasks based on a library of probabilistic skill models. Motions can be combined to accomplish complex tasks in hybrid domains. Our approach is validated in a variety of case studies, including an Android game, simulated assembly task, and real robot experiment with a UR5.
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Date   2016-02-15
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arXiv  1602.04754v1
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