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Multi-legged robots offer enhanced stability in complex terrains, yetautonomously learning natural and robust motions in such environments remainschallenging. Drawing inspiration from animals' progressive learning patterns,from simple to complex tasks, we introduce a universal two-stage learningframework with two-step reward setting based on self-acquired experience, whichefficiently enables legged robots to incrementally learn natural and robustmovements. In the first stage, robots learn through gait-related rewards totrack velocity on flat terrain, acquiring natural, robust movements andgenerating effective motion experience data. In the second stage, mirroringanimal learning from existing experiences, robots learn to navigate challengingterrains with natural and robust movements using adversarial imitationlearning. To demonstrate our method's efficacy, we trained both quadrupedrobots and a hexapod robot, and the policy were successfully transferred to aphysical quadruped robot GO1, which exhibited natural gait patterns andremarkable robustness in various terrains.

Experience-Learning Inspired Two-Step Reward Method for Efficient Legged Locomotion Learning Towards Natural and Robust Gaits