  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.
