OSMO: Open-Source Tactile Glove for Human-to-Robot Skill Transfer

TL;DR

OSMO is an open-source tactile glove that enables effective human-to-robot skill transfer for contact-rich manipulation tasks by capturing tactile feedback directly from human demonstrations.

Contribution

The paper introduces OSMO, a wearable tactile glove that facilitates direct transfer of tactile signals from humans to robots, improving manipulation performance without relying on vision-based force inference.

Findings

Tactile-aware policy achieves 72% success rate on wiping task.

OSMO enables transfer of continuous shear and normal force feedback.

Outperforms vision-only baselines in contact-rich manipulation.

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.