UMI on Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllers

Huy Ha, Yihuai Gao, Zipeng Fu, Jie Tan, Shuran Song

Keywords: Visuomotor Policy, Whole-body Control, Cross-Embodiment

TL;DR: A framework for scalable quadruped manipulation, combining manipulation policies trained from real-world demonstrations with whole-body controllers trained from simulation.

Abstract: We introduce UMI-on-Legs, a new framework that combines real-world and simulation data for quadruped manipulation systems. We scale task-centric data collection in the real world using a handheld gripper (UMI), providing a cheap way to demonstrate task-relevant manipulation skills without a robot. Simultaneously, we scale robot-centric data in simulation by training a whole-body controller. The interface between these two policies are end-effector trajectories in the task-frame, which are inferred by the manipulation policy and passed to the whole-body controller for tracking. We evaluate UMI-on-Legs on prehensile, non-prehensile, and dynamic manipulation tasks, and report over 70% success rate for all tasks. Lastly, we also demonstrate the zero-shot cross-embodiment deployment of a pre-trained manipulation policy checkpoint from a prior work, originally intended for a fixed-base robot arm, on our quadruped system. We believe this framework provides a scalable path towards learning expressive manipulation skills on dynamic robot embodiments.

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Submission Number: 3