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In recent years, Reinforcement Learning (RL) is becoming a popular techniquefor training controllers for robots. However, for complex dynamic robot controltasks, RL-based method often produces controllers with unrealistic styles. Incontrast, humans can learn well-stylized skills under supervisions. Forexample, people learn table tennis skills by imitating the motions of coaches.Such reference motions are often incomplete, e.g. without the presence of anactual ball. Inspired by this, we propose an RL-based algorithm to train arobot that can learn the playing style from such incomplete humandemonstrations. We collect data through the teaching-and-dragging method. Wealso propose data augmentation techniques to enable our robot to adapt to ballsof different velocities. We finally evaluate our policy in different simulatorswith varying dynamics.

Stylized Table Tennis Robots Skill Learning with Incomplete Human Demonstrations