目标脚步序列引导的人形机器人步态生成和运动控制OA

To address the problem of environmental constraints on foot placement for humanoid robots when walking on complex terrain,a reinforcement learning-based gait generation and motion control method guided by a target footstep sequence was proposed.In the reinforcement learning framework,target footstep positions and robot body orientation were incorporated into the observation input,and a reward mechanism combining dense and sparse rewards was designed to guide the robot to satisfy foot placement constraints while maintaining gait stability.Multi-scenario simulation experiments were conducted on the Unitree G1 humanoid robot platform.The results showed that the robot could autonomously learn stable walking strategies without relying on expert demonstration data and achieved high-precision foot placement on four types of complex terrain,including obstacles,uneven surfaces,stepping stones,and gaps,verifying the effectiveness of the proposed method.The study results provide a reliable motion control foundation for highly dynamic autonomous navigation of humanoid robots in complex environments.