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获弹簧加载双足机器人的动力学。这一点很重要，因为机器人的顺应性使得建模分析动力学变得困难。利用所学的动力学，我们成功地在仿真环境中进行了实时规划的跳跃任务。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，实现了一个简单的步行任务。预计在接下来的一年里，业绩将显著改善。

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.