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

By Sammy Joe Christen, Stefan Stevšić, Otmar Hilliges. 2019

26 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Control and Systems Engineering, Social Psychology

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Citations in the corpus

Solving Rubik's Cube with a Robot Hand (2019, background): “Some researchers have successfully utilized expert human demonstrations in guiding the training process of the agents [17, 63, 4, 122].”