Multi-legged robots offer enhanced stability in complex terrains, yet autonomously learning natural and robust motions in such environments remains challenging. Drawing inspiration from animals' progressive learning patterns, from simple to complex tasks, we introduce a universal two-stage learning framework with two-step reward setting based on self-acquired experience, which efficiently enables legged robots to incrementally learn natural and robust movements. In the first stage, robots learn through gait-related rewards to track velocity on flat terrain, acquiring natural, robust movements and generating effective motion experience data. In the second stage, mirroring animal learning from existing experiences, robots learn to navigate challenging terrains with natural and robust movements using adversarial imitation learning. To demonstrate our method's efficacy, we trained both quadruped robots and a hexapod robot, and the policy were successfully transferred to a physical quadruped robot GO1, which exhibited natural gait patterns and remarkable robustness in various terrains.

翻译：多足机器人在复杂地形中具有显著稳定性，但要让其自主学会自然鲁棒的运动模式仍具挑战性。受动物从简单到复杂任务的递进式学习模式启发，我们提出了一种通用两阶段学习框架，该框架基于自获取经验设计两步奖励设置，能高效实现腿式机器人逐步学习自然鲁棒的运动。在第一阶段，机器人通过步态相关奖励在平坦地形上学习速度跟踪，获取自然鲁棒的运动模式并生成有效运动经验数据。在第二阶段，模拟动物从已有经验中学习的过程，机器人利用对抗性模仿学习掌握在复杂地形中保持自然鲁棒运动的导航能力。为验证方法有效性，我们训练了四足机器人与六足机器人，并将策略成功迁移至物理四足机器人GO1，该机器人在多种地形中展现出自然的步态模式与卓越的鲁棒性。