Learning-Based Hybrid Locomotion Control for a Heavy Wheeled-Legged Robot in Challenging Terrains

Wheeled-legged robots have gained attention for their potential to combine the efficiency of wheels with the adaptability of legs. However, most prior research has focused on lightweight platforms. In this work, we present the first successful deployment of a reinforcement learning (RL) controller on a heavy wheeled-legged robot weighing over 340 kg, which, to the best of our knowledge, is one of the largest and heaviest wheeled-legged robots reported in the literature to demonstrate RL-based hybrid locomotion control. We propose a robust end-to-end control framework, incorporating a heuristic reward structure and a state estimator trained via privileged learning. Simulation experiments demonstrate accurate speed tracking performance, strong robustness to external disturbances, and graceful performance degradation under slow-response actuation. Field experiments, including plateau trials on gobi deserts, gravel, meadows, and wetlands, further demonstrate the policy’s robustness, terrain adaptability, and real-world deployability, with a maximum measured speed of 3.80 m/s achieved in outdoor tests. These simulation and field results establish a benchmark for deploying RL on heavy robotic systems under real-world constraints.