MULE – Multi-Terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion

Abstract

Quadrupedal robots deployed for load-carrying applications must maintain stable locomotion across diverse ter- rains and varying payloads. Traditional approaches like Model Predictive Control (MPC) can handle such variations but often rely on predefined gait schedules and manually tuned trajectory planners, limiting adaptability in unstructured environments. To address this, we propose an adaptive reinforcement learning (RL) framework that enables quadrupedal robots to respond dynamically to terrain and payload changes without relying on contact force measurements or gait designs. The controller con- sists of a nominal policy that learns general locomotion across terrains and an adaptive policy that outputs corrective actions for handling dynamic variations due to payloads. We validate our approach through extensive simulations in Isaac Gym across payloads (2–10 kg) and terrains including flat ground, slopes, and stairs. Our method achieves higher success rates and lower height-tracking errors while maintaining the Cost of Transport (CoT) comparable to the best-performing baselines and to no-load (NL) operation. Real-world deployment on a Unitree Go1 confirms the approach’s effectiveness under both static and dynamic payload changes, including freely moving masses. The policy also performs well on outdoor terrains such as grass, soil, and staircases. The adaptive policy modulates corrections based on payload changes, improving body stability and tracking without post-deployment fine-tuning.