用局部模仿学习让四足机械臂更稳地推车
Partial Motion Imitation for Learning Cart Pushing with Legged Manipulators
- 只模仿下肢动作，融合对抗性运动先验提升稳定性
- 在IsaacLab和MuJoCo中成功实现多样轨迹推车，表现优于基线
- 适合研究四足机器人移动操作或仿生控制的开发者
腿式机器人执行实际移动操作任务（如搬运、推动物体）需要具备良好的运动-操作协同能力。然而，在保持稳定行走的同时完成精准操作仍具挑战。本文提出一种局部模仿学习方法，将仅通过运动任务训练获得的运动风格迁移至推车任务。首先在大量领域与地形随机化条件下训练出鲁棒的运动策略，再通过仅模仿下肢动作并结合部分对抗性运动先验，学习运动-操作策略。实验表明，该策略在IsaacLab中可沿多样化轨迹成功推动手推车，并有效迁移到MuJoCo环境。与多个基线对比显示，所提方法在运动-操作协同上更具稳定性和准确性。
原文摘要 · Abstract (English)
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.
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