OSMO: Open-Source Tactile Glove for Human-to-Robot Skill Transfer
This addresses the challenge of contact-rich manipulation for robotics by providing a low-cost, open-source tool to bridge the tactile gap between humans and robots, though it is incremental as it builds on existing hand-tracking methods.
The paper tackled the problem of transferring human manipulation skills to robots by introducing OSMO, an open-source tactile glove that captures contact signals from human demonstrations, enabling a robot policy trained solely on this data to achieve a 72% success rate on a real-world wiping task.
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.