Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots

TL;DR

This paper introduces a learning-based framework for wheel-leg coordinated recovery in fallen robots, significantly improving robustness and efficiency through dynamic reward shaping and curriculum learning.

Contribution

It proposes a novel learning framework with dynamic reward shaping and curriculum learning for robust recovery in wheeled-legged robots, outperforming traditional methods.

Findings

Recovery success rates up to 99.1% and 97.8%.

Joint torque consumption reduced by 15.8% and 26.2%.

Enhanced robustness with noise-injected observations.

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/