MV-UMI: A Scalable Multi-View Interface for Cross-Embodiment Learning

• URL: http://arxiv.org/abs/2509.18757v1
• Date: Tue, 23 Sep 2025 07:53:05 GMT
• Title: MV-UMI: A Scalable Multi-View Interface for Cross-Embodiment Learning
• Authors: Omar Rayyan, John Abanes, Mahmoud Hafez, Anthony Tzes, Fares Abu-Dakka,
• Abstract summary: We present MV-UMI (Multi-View Universal Manipulation Interface), a framework that integrates a third-person perspective with the egocentric camera.<n>This integration mitigates domain shifts between human demonstration and robot deployment, preserving the cross-embodiment advantages of handheld data-collection devices.
• Score: 3.079859911926098
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in imitation learning have shown great promise for developing robust robot manipulation policies from demonstrations. However, this promise is contingent on the availability of diverse, high-quality datasets, which are not only challenging and costly to collect but are often constrained to a specific robot embodiment. Portable handheld grippers have recently emerged as intuitive and scalable alternatives to traditional robotic teleoperation methods for data collection. However, their reliance solely on first-person view wrist-mounted cameras often creates limitations in capturing sufficient scene contexts. In this paper, we present MV-UMI (Multi-View Universal Manipulation Interface), a framework that integrates a third-person perspective with the egocentric camera to overcome this limitation. This integration mitigates domain shifts between human demonstration and robot deployment, preserving the cross-embodiment advantages of handheld data-collection devices. Our experimental results, including an ablation study, demonstrate that our MV-UMI framework improves performance in sub-tasks requiring broad scene understanding by approximately 47% across 3 tasks, confirming the effectiveness of our approach in expanding the range of feasible manipulation tasks that can be learned using handheld gripper systems, without compromising the cross-embodiment advantages inherent to such systems.

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