  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 hand-held 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
whole-body controller for task-tracking without task simulation setups. The
interface between these two policies is end-effector trajectories in the task
frame, 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 on all tasks.
Lastly, we demonstrate the zero-shot cross-embodiment deployment of a
pre-trained manipulation policy checkpoint from 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. Please checkout our website for robot videos, code,
and data: https://umi-on-legs.github.io
