Development of Bipedal Locomotion Controller with Model-based Reinforcement Learning

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During this year, we utilized model-based reinforcement learning to capture the dynamics of a spring-loaded biped robot. This is significant because the robot's compliancy makes modeling analytic dynamics difficult. Using the learned dynamics, we successfully performed hopping tasks with real-time planning in a simulation environment. These results demonstrate the effectiveness of our approach. Additionally, we have begun hardware implementation, achieving a simple walking task. The performance is expected to significantly improve in the following year.