Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots
Journal:
arXiv
Published Date:
Jun 5, 2025
Abstract
Adaptive recovery from fall incidents are essential skills for the practical
deployment of wheeled-legged robots, which uniquely combine the agility of legs
with the speed of wheels for rapid recovery. However, traditional methods
relying on preplanned recovery motions, simplified dynamics or sparse rewards
often fail to produce robust recovery policies. This paper presents a
learning-based framework integrating Episode-based Dynamic Reward Shaping and
curriculum learning, which dynamically balances exploration of diverse recovery
maneuvers with precise posture refinement. An asymmetric actor-critic
architecture accelerates training by leveraging privileged information in
simulation, while noise-injected observations enhance robustness against
uncertainties. We further demonstrate that synergistic wheel-leg coordination
reduces joint torque consumption by 15.8% and 26.2% and improves stabilization
through energy transfer mechanisms. Extensive evaluations on two distinct
quadruped platforms achieve recovery success rates up to 99.1% and 97.8%
without platform-specific tuning. The supplementary material is available at
https://boyuandeng.github.io/L2R-WheelLegCoordination/
