Loco-manipulation is a key capability for legged robots to perform practical mobile manipulation tasks, such as transporting and pushing objects, in real-world environments. However, learning robust loco-manipulation skills remains challenging due to the difficulty of maintaining stable locomotion while simultaneously performing precise manipulation behaviors. This work proposes a partial imitation learning approach that transfers the locomotion style learned from a locomotion task to cart loco-manipulation. A robust locomotion policy is first trained with extensive domain and terrain randomization, and a loco-manipulation policy is then learned by imitating only lower-body motions using a partial adversarial motion prior. We conduct experiments demonstrating that the learned policy successfully pushes a cart along diverse trajectories in IsaacLab and transfers effectively to MuJoCo. We also compare our method to several baselines and show that the proposed approach achieves more stable and accurate loco-manipulation behaviors.

翻译：腿式机械手的操作能力是腿式机器人执行实际移动操作任务（如运输和推动物体）的关键能力。然而，由于在保持稳定移动的同时执行精细操作行为存在困难，学习鲁棒的腿式操作技能仍然具有挑战性。本文提出了一种部分模仿学习方法，将移动任务中学到的运动风格迁移到推车腿式操作中。首先通过广泛的领域和地形随机化训练鲁棒的移动策略，然后通过部分对抗性运动先验仅模仿下半身动作来学习腿式操作策略。实验表明，学习到的策略成功在IsaacLab中沿多种轨迹推车，并有效迁移到MuJoCo。我们还将方法与多种基线进行比较，表明所提方法实现了更稳定和精确的腿式操作行为。