Integrated Whole-Body Loco-Manipulation Policy for Sliding Heavy Objects with a Single-Arm Quadruped Robot
This paper presents an end-to-end deep reinforcement learning (DRL) framework for integrated whole-body loco-manipulation control of a single-arm quadrupedal robot in contact-rich tasks. A single policy simultaneously controls all 18 joints of a Unitree Go2 robot equipped with a 6-DoF PiPER arm, trained in NVIDIA Isaac Lab using massively parallel simulation. The framework is evaluated on three contact-rich tasks: heavy-object dragging (up to 15 kg), heavy-object pushing, and elongated-object extraction from stacked configurations. The learned policy produces coordinated whole-body behaviors, where the legs provide propulsion and posture stabilization while the arm maintains task-oriented interaction with the object under strong contact forces. To investigate cross-simulator robustness, policies trained in Isaac Lab are directly evaluated in MuJoCo over 1,000 episodes under different domain randomization settings, showing that disturbance-aware training at the robot base achieves a 78.5% success rate in the dragging task and substantially outperforms friction and mass randomization alone.