RobotMover: Learning to Move Large Objects From Human Demonstrations

Abstract

Moving large objects, such as furniture or appliances, is a critical capability for robots operating in human environments. This task presents unique challenges, including whole-body coordination to avoid collisions and managing the underactuated dynamics of bulky, heavy objects. In this work, we present RobotMover, a complete learning-based system for large-object manipulation that leverages human–object interaction demonstrations to train robot control policies. RobotMover formulates the manipulation problem as imitation learning using a simplified spatial representation—referred to as the interaction chain—to capture essential human–object interaction dynamics in a morphology-agnostic way. We integrate this interaction chain into a reward structure and train policies in simulation using domain randomization to support zero-shot transfer to real-world hardware. The learned policies enable a spot robot to manipulate various large objects—including chairs, tables, and standing lamp. Through extensive experiments across simulation and real-world platforms, we demonstrate that RobotMover achieves strong performance in terms of capability, robustness, and controllability, outperforming both learned and teleoperation baselines. Our system further supports practical applications by combining the learned policy with simple planning modules to accomplish long-horizon object transport and rearrangement tasks in the real world.