LORM: a novel reinforcement learning framework for biped gait control

Legged robots are better able to adapt to different terrains compared with wheeled robots. However, traditional motion controllers suffer from extremely complex dynamics properties. Reinforcement learning (RL) helps to overcome the complications of dynamics design and calculation. In addition, the high autonomy of the RL controller results in a more robust response to complex environments and terrains compared with traditional controllers. However, RL algorithms are limited by the problems of convergence and training efficiency due to the complexity of the task. Learn and outperform the reference motion (LORM), an RL based framework for gait controlling of biped robot is proposed leveraging the prior knowledge of reference motion. The proposed trained agent outperformed the reference motion and existing motion-based methods. The RL environment was finely crafted for optimal performance, including the pruning of state space and action space, reward shaping, and design of episode criterion. Several improvements were implemented to further improve the training efficiency and performance including: random state initialization (RSI), the noise of joint angles, and a novel improvement based on symmetrization of gait. To validate the proposed method, the Darwin-op robot was set as the target platform and two different tasks were designed: (I) Walking as fast as possible and (II) Tracking specific velocity. In task (I), the proposed method resulted in the walking velocity of 0.488 m/s, with a 5.8 times improvement compared with the original traditional reference controller. The directional accuracy improved by 87.3%. The velocity performance achieved 2x compared with the rated max velocity and more than 8x compared with other recent works. To our knowledge, our work achieved the best velocity performance on the platform Darwin-op. In task (II), the proposed method achieved a tracking accuracy of over 95%. Different environments are introduced including plains, slopes, uneven terrains, and walking with external force, where the robot was expected to maintain walking stability with ideal speed and little direction deviation, to validate the performance and robustness of the proposed method.

Automated summary

A gait controlling framework based on reinforcement learning is proposed, leveraging the prior knowledge of reference motion to train the robot. The trained agent outperforms traditional methods and achieves improved performance through optimization in a finely crafted RL environment. The proposed method is validated in different tasks and environments, demonstrating its performance and robustness.