Reducing Human Efforts in Reinforcement Learning for Legged Skill Development

Abstract

In the first work, we propose a multi-stage training structure that decouples the training of specific tasks into repurposable low-level skill primitive development and task-specific high-level policy learning. In the second work, we introduce a novel adversarial imitation learning method that allows skill learning from rough, partial, hand-held demonstrations. In the last work, we combine the first two ideas and develop versatile, skill-conditioned policies from unlabeled, mixed motion references by integrating unsupervised skill discovery techniques into adversarial imitation learning settings.

Chenhao Li

Reinforcement Learning for Robotics

My research interests focus on the general field of robot learning, including reinforcement learning, developmental robotics and legged intelligence.