Abstract
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.
Abstract (translated)
人类视频演示为学习机器人策略提供了丰富的训练数据，但仅靠视频无法捕捉到掌握操作技能所需的丰富接触信号。为此，我们引入了OSMO——一款开源可穿戴触觉手套，旨在促进从人到机器人的技能转移。这款手套在指尖和手掌上配备了12个三轴触觉传感器，并设计为与最先进的手部追踪方法兼容，以便于野外数据采集。 实验表明，在仅使用配备OSMO的手套收集的人类演示数据训练机器人策略的情况下，该机器人能够执行一项具有挑战性的接触密集型操作任务。通过让人类和机器人都佩戴相同的触觉手套，OSMO减少了视觉和触觉感知差距，使连续切向力和法向力反馈的传输成为可能，并且避免了图像修复或其他基于视觉的方法来推断力的需求。 在需要持续接触压力的真实世界擦拭任务中，我们的具备触觉感知能力的策略达到了72%的成功率，优于仅依赖视觉的基础模型。通过消除与接触相关的问题模式，该系统显著提高了性能。 我们发布了完整的硬件设计、固件以及组装说明，以支持社区采用这一技术。
URL
https://arxiv.org/abs/2512.08920
