Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Composition
Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Composition
We study the problem of realizing the full spectrum of bipedal locomotion on a real robot with sim-to-real reinforcement learning (RL). A key challenge of learning legged locomotion is describing different gaits, via reward functions, in a way that is intuitive for the designer and specific enough to reliably learn …