Teacher-Guided Terrain-Aware Learning for Recovery of Quadruped Robots

Abstract

Autonomous post-fall recovery is a key bottleneck for long-duration deployment of legged robots, yet prevailing methods depend on flat-ground target poses and heuristic rewards and thus fail to generalize under irregular terrain and random initial states. We introduce a teacher-guided, terrain-aware recovery framework, which relies solely on proprioception to drive a quadruped from arbitrary fallen configurations on uneven ground to an optimal posture. We formally define the terrain-adapted optimal recovery posture by formulating a kinematic–dynamic objective and evaluate outcomes using a unified force–angle stability margin. Building on this, a Teacher–Student PPO scheme distills the teacher’s privileged terrain knowledge into a deployable student using imitation losses over short proprioceptive histories. Extensive simulation and hardware trials show a recovery success rate of 90.26% across diverse rough terrains and randomized states, with the teacher at 93.47% and the baseline at 78.6%, while stability margins remain positive in all successful trials, indicating reliable static stability of the final posture. The policy maintains robustness in extreme initial conditions and low-friction settings through repeated rolling and rocking. These results close gaps in the formalization of target postures and in evaluation standards for non-flat recovery and deliver a general, deployable proprioception-only paradigm for fall recovery on complex terrain.