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/

翻译：动物利用四肢同时实现移动与操作功能。本研究旨在为四足机器人赋予类似的通用能力。本文提出一种使四足机器人能够利用腿部与物体交互的系统，其设计灵感来源于非抓取式操作。该系统包含两个核心模块：视觉操作策略模块与移动-操作器模块。视觉操作策略通过强化学习训练，基于点云观测和以物体为中心的动作决策腿部与物体的交互方式；移动-操作器控制器则基于阻抗控制与模型预测控制，协调腿部运动与机体姿态调整。除单腿操作外，该系统还能根据评估器图谱选择左/右腿，并通过基座调整将物体移动至远距离目标。实验在仿真与真实环境中对物体位姿对齐任务进行了系统评估，结果表明本系统相比先前工作实现了更通用的腿部物体操作能力。演示视频可见：https://legged-manipulation.github.io/