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arXiv · 1906.11695

Demonstration-Guided Deep Reinforcement Learning of Control Policies for Dexterous Human-Robot Interaction

Sammy Christen·Stefan Stevsic·Otmar Hilliges
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Abstract

In this paper, we propose a method for training control policies for human-robot interactions such as handshakes or hand claps via Deep Reinforcement Learning. The policy controls a humanoid Shadow Dexterous Hand, attached to a robot arm. We propose a parameterizable multi-objective reward function that allows learning of a variety of interactions without changing the reward structure. The parameters of the reward function are estimated directly from motion capture data of human-human interactions in order to produce policies that are perceived as being natural and human-like by observers. We evaluate our method on three significantly different hand interactions: handshake, hand clap and finger touch. We provide detailed analysis of the proposed reward function and the resulting policies and conduct a large-scale user study, indicating that our policy produces natural looking motions.

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Sammy Christen, Stefan Stevsic, Otmar Hilliges. 2019-06-27. Demonstration-Guided Deep Reinforcement Learning of Control Policies for Dexterous Human-Robot Interaction. https://doi.org/10.1109/icra.2019.8794065

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