基于再励学习的被动动态步行机器人

Reinforcement learning for passive dynamic walking robot

为了研究仿人、能量高效的双足机器人步行,研制了由MACCEPA(mechanically adjustable compliance and controllable equilibrium position actuator)柔性驱动器驱动的半被动双足机器人,并实现了其动力学仿真系统。提出一种基于再励学习的步行控制方法。该方法首先采用Q-学习方法学习机器人在理想环境中的稳定步行步态及其控制策略,然后将此步态和控制策略作为模糊优胜学习方法的参考步态和参考控制策略并在线学习模糊网络的优胜值参数。仿真结果表明:利用学习训练的结果控制柔性驱动器在步行相转换时的动作,机器人可以实现稳定动态步行。

A quasi-passive dynamic walking robot was built to study natural, energy-efficient biped walking. The robot was actuated by mechanically adjustable compliance and controllable equilibrium position actuators （MACCEPA）. A reinforcement learning based method was used to control the robot to walk. The method firstly learned the desired gait for walking in ideal environment with a gait model based Q-learning algorithm. Then, a fuzzy advantage learning method was used to teach the robot to walk in uneven floor. Stable walking of the robot is achieved by using the learning result to control the action of the actuators when changes occur in the walking phase. The effectiveness of the method was verified by simulations.