四足机器人用腿抓物，像动物一样边走边操作。
Visual Manipulation with Legs
- 用强化学习训练视觉操控策略，决定腿如何碰物体
- 实测能单腿搬动物体，还支持换腿和远程移动目标
- 适合做野外作业或复杂地形的机器人操控研究
动物通过四肢实现移动与操作的双重功能。本文旨在赋予四足机器人类似能力，提出一套系统，使四足机器人可利用腿部完成非抓握式物体操作。系统包含两个核心模块：基于点云观测与以物体为中心动作的强化学习视觉操控策略，以及基于阻抗控制与模型预测控制（MPC）的运动-操控控制器。该系统不仅能用单条腿操控物体，还可根据评价图选择左右腿，并通过基座调整将物体移动至远距离目标。在仿真与真实世界中，针对物体姿态对齐任务的实验验证了其优于以往方法的多样化操控能力。视频展示见 https://legged-manipulation.github.io/
原文摘要 · Abstract (English)
Animals use limbs for both locomotion and manipulation. We aim to equip quadruped robots with similar versatility. This work introduces a system that enables quadruped robots to interact with objects using their legs, inspired by non-prehensile manipulation. The system has two main components: a visual manipulation policy module and a loco-manipulator module. The visual manipulation policy, trained with reinforcement learning (RL) using point cloud observations and object-centric actions, decides how the leg should interact with the object. The loco-manipulator controller manages leg movements and body pose adjustments, based on impedance control and Model Predictive Control (MPC). Besides manipulating objects with a single leg, the system can select from the left or right leg based on critic maps and move objects to distant goals through base adjustment. Experiments evaluate the system on object pose alignment tasks in both simulation and the real world, demonstrating more versatile object manipulation skills with legs than previous work. Videos can be found at https://legged-manipulation.github.io/
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