用手套捕捉触觉信号，让机器人学会复杂抓握动作。
OSMO: Open-Source Tactile Glove for Human-to-Robot Skill Transfer
- 用12个三轴传感器手套采集人类操作时的触觉数据。
- 仅靠人类示范数据训练出的机器人策略成功率达72%。
- 适合做具身智能、人机协作与触觉感知研究者参考。
人类视频演示为学习机器人策略提供了丰富数据，但视频无法捕捉操控中关键的接触信号。我们提出OSMO，一款开源可穿戴触觉手套，用于人到机器人的技能迁移。手套在指尖和掌面布置了12个三轴触觉传感器，兼容主流手部追踪方法，支持野外数据采集。实验表明，仅使用OSMO采集的人类示范数据训练机器人策略，无需真实机器人数据，即可完成高难度的接触密集型操作任务。通过在人与机器人上配备相同手套，OSMO缩小了视觉与触觉的体感差距，实现连续剪切力与法向力反馈的直接传递，避免图像修复或基于视觉的力推断。在需要持续接触压力的真实擦除任务中，触觉感知策略成功率高达72%，显著优于仅依赖视觉的基线方法，有效消除接触相关失败模式。我们开源了完整的硬件设计、固件及组装说明，以促进社区应用。
原文摘要 · Abstract (English)
Human video demonstrations provide abundant training data for learning robot policies, but video alone cannot capture the rich contact signals critical for mastering manipulation. We introduce OSMO, an open-source wearable tactile glove designed for human-to-robot skill transfer. The glove features 12 three-axis tactile sensors across the fingertips and palm and is designed to be compatible with state-of-the-art hand-tracking methods for in-the-wild data collection. We demonstrate that a robot policy trained exclusively on human demonstrations collected with OSMO, without any real robot data, is capable of executing a challenging contact-rich manipulation task. By equipping both the human and the robot with the same glove, OSMO minimizes the visual and tactile embodiment gap, enabling the transfer of continuous shear and normal force feedback while avoiding the need for image inpainting or other vision-based force inference. On a real-world wiping task requiring sustained contact pressure, our tactile-aware policy achieves a 72% success rate, outperforming vision-only baselines by eliminating contact-related failure modes. We release complete hardware designs, firmware, and assembly instructions to support community adoption.
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