Tool-as-Interface: Learning Robot Policies from Observing Human Tool Use

Abstract

Tool use is essential for enabling robots to perform complex real-world tasks, but learning such skills requires extensive datasets. While teleoperation is widely used, it is slow, delay-sensitive, and poorly suited for dynamic tasks. In contrast, human videos provide a natural way for data collection without specialized hardware, though they pose challenges on robot learning due to viewpoint variations and embodiment gaps. To address these challenges, we propose a framework that transfers tool-use knowledge from humans to robots. To improve the policy’s robustness to viewpoint variations, we use two RGB cameras to reconstruct 3D scenes and apply Gaussian splatting for novel view synthesis. We reduce the embodiment gap using segmented observations and tool-centric, task-space actions to achieve embodiment-invariant visuomotor policy learning. Our method achieves a 71% improvement in task success and a 77% reduction in data collection time compared to diffusion policies trained on teleoperation with equivalent time budgets. Our method also reduces data collection time by 41% compared with the state-of-the-art data collection interface.