Stability Control of Biped Robot on Static and Dynamic Platforms Based on Hybrid Reinforcement Learning

In this work, we proposed a novel Hybrid Reinforcement Learning algorithm that incorporates the model-based reinforcement learning scheme with the model-free reinforcement learning framework, where the sample efficiency was improved while still retaining the desired control performance. The proposed learning framework was successfully applied to a complex biped robot model, where the robot was able to maintain balance on a rotating platform. Experiment results also showed that the proposed algorithm was able to generate stable walking gaits for the robot to walk on both static and dynamic platforms without falling down.