Tool-as-Interface: Learning Robot Policies from Human Tool Usage through Imitation Learning

2025-04-06

AI Summary

To address low teleoperation efficiency, high latency, and poor adaptability to dynamic tasks in transferring human tool manipulation knowledge to robots, this paper proposes the “Tool-as-Interface” paradigm. It takes only natural human tool-use videos as input, employs dual-RGB-camera 3D reconstruction and Gaussian splatting-based view augmentation, extracts embodiment-agnostic observations via semantic segmentation, and explicitly encodes tool motions in task space to enable end-to-end visuomotor policy learning. This approach establishes the first physical coupling between humans and robots sharing identical tools, fundamentally bridging the embodiment gap and enabling cross-view, cross-robot-configuration, and cross-object generalization. Evaluated on tasks including meatball scooping, frying pan flipping, and wine bottle balancing, our method achieves a 71% average success rate improvement. Data collection time is reduced by 77% versus teleoperation and by 41% versus hand-held gripper annotation; several tasks succeed exclusively with our method.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Interaction Techniques and Devices
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Tool use is critical for enabling robots to perform complex real-world tasks, and leveraging human tool-use data can be instrumental for teaching robots. However, existing data collection methods like teleoperation are slow, prone to control delays, and unsuitable for dynamic tasks. In contrast, human natural data, where humans directly perform tasks with tools, offers natural, unstructured interactions that are both efficient and easy to collect. Building on the insight that humans and robots can share the same tools, we propose a framework to transfer tool-use knowledge from human data to robots. Using two RGB cameras, our method generates 3D reconstruction, applies Gaussian splatting for novel view augmentation, employs segmentation models to extract embodiment-agnostic observations, and leverages task-space tool-action representations to train visuomotor policies. We validate our approach on diverse real-world tasks, including meatball scooping, pan flipping, wine bottle balancing, and other complex tasks. Our method achieves a 71% higher average success rate compared to diffusion policies trained with teleoperation data and reduces data collection time by 77%, with some tasks solvable only by our framework. Compared to hand-held gripper, our method cuts data collection time by 41%. Additionally, our method bridges the embodiment gap, improves robustness to variations in camera viewpoints and robot configurations, and generalizes effectively across objects and spatial setups.

Problem

• Transfer human tool-use knowledge to robots efficiently
• Overcome limitations of teleoperation in dynamic tasks
• Bridge embodiment gap for robust policy generalization

Innovation

• Uses RGB cameras for 3D reconstruction
• Applies Gaussian splatting for view augmentation
• Leverages task-space tool-action representations