  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.
