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This paper presents a handheld interface with a scissor-shaped mechanism that returns contact forces to the operator's hands, aimed at collecting demonstrations for robot policy learning. The force feedback is mechanically transmitted rather than sensed, and the reported gains are efficiency improvements of roughly 18-29% over baselines across four tasks, with results still limited to the authors' own evaluations. For ML readers, the key question is whether richer haptic signals in demonstrations translate into better imitation learning on contact-rich tasks in new settings.
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FOMI: A Force-Feedback Manipulation Interface for Teaching Contact-Rich Manipulation

arXiv2 min read

arXiv:2610.19839v1 Announce Type: new Abstract: Robot-free collection with handheld interfaces enables scalable acquisition of human demonstrations for policy learning. However, existing handheld interfaces often provide limited intuitive force feedback, making contact-rich demonstration collection challenging for human operators. To address this, we present FOMI, a force-feedback manipulation interface with a scissor-shaped mechanism, which transmits object interaction forces directly to the operator's hands through actuation links without force sensors. This intuitive force feedback supports real-time action adjustment during robot-free collection of contact-rich demonstrations. Around FOMI, we build a demonstration collection system that integrates sensor calibration, data acquisition, and synchronization for robot-free collection. By further mapping pose changes and gripper opening of FOMI to robot end-effector poses and gripper commands, the system also supports teleoperation. Across four contact-rich tasks, FOMI improves collection efficiency by approximately 29% and 18% over the best baselines in robot-free collection and teleoperation, respectively. Demonstrations collected with FOMI in both modes enable policies to perform challenging contact-rich manipulation tasks with high success rates.

Abstract:Robot-free collection with handheld interfaces enables scalable acquisition of human demonstrations for policy learning. However, existing handheld interfaces often provide limited intuitive force feedback, making contact-rich demonstration collection challenging for human operators. To address this, we present FOMI, a force-feedback manipulation interface with a scissor-shaped mechanism, which transmits object interaction forces directly to the operator's hands through actuation links without force sensors. This intuitive force feedback supports real-time action adjustment during robot-free collection of contact-rich demonstrations. Around FOMI, we build a demonstration collection system that integrates sensor calibration, data acquisition, and synchronization for robot-free collection. By further mapping pose changes and gripper opening of FOMI to robot end-effector poses and gripper commands, the system also supports teleoperation. Across four contact-rich tasks, FOMI improves collection efficiency by approximately 29% and 18% over the best baselines in robot-free collection and teleoperation, respectively. Demonstrations collected with FOMI in both modes enable policies to perform challenging contact-rich manipulation tasks with high success rates.

Robotics (cs.RO)

arXiv:2610.19839 [cs.RO]

(or arXiv:2610.19839v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.19839 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 252 words

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