Development of Bipedal Locomotion Controller with Model-based Reinforcement Learning

在这一年中，我们利用基于模型的强化学习来捕捉弹簧加载的机器人的动态。这是重要的，因为机器人的顺应性使得建模分析动力学困难。使用学到的动态，我们成功地执行跳跃任务，在仿真环境中的实时规划。这些结果证明了我们方法的有效性。此外，我们已经开始硬件实现，实现一个简单的步行任务。预期来年业绩将大幅改善。

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.