Learning natural locomotion behaviors for humanoid robots using human knowledge
This work addresses the challenge of creating dynamic and natural locomotion for humanoid robots, which is incremental as it builds on existing methods by integrating human knowledge.
The paper tackled the problem of achieving natural and robust humanoid locomotion by developing a learning framework that combines imitation learning, deep reinforcement learning, and control theories, resulting in robust policies that handle disturbances like terrain irregularities and external pushes.
This paper presents a new learning framework that leverages the knowledge from imitation learning, deep reinforcement learning, and control theories to achieve human-style locomotion that is natural, dynamic, and robust for humanoids. We proposed novel approaches to introduce human bias, i.e. motion capture data and a special Multi-Expert network structure. We used the Multi-Expert network structure to smoothly blend behavioral features, and used the augmented reward design for the task and imitation rewards. Our reward design is composable, tunable, and explainable by using fundamental concepts from conventional humanoid control. We rigorously validated and benchmarked the learning framework which consistently produced robust locomotion behaviors in various test scenarios. Further, we demonstrated the capability of learning robust and versatile policies in the presence of disturbances, such as terrain irregularities and external pushes.