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arXiv · 2610.07217

RoboCap: A New Platform for Egocentric Robot Learning

Qasim Ali·Nikash Bhardwaj·Kheri Hughes·Sam Cho·Ethan Lee·Paul Luo·Shinn Chong·Sunny Yu·Owen Akhibi·Alex Proshkin·Haotian Zhan·Niresh Dravin·Vivek Sharma·Jonathan Victor·Amanda Young·Michael Cho·Ademi Adeniji·Vincent Liu
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Abstract

Despite its promise for scaling robot learning, egocentric manipulation data is still scarce today. Collection at scale requires vertically integrating ergonomic hardware with centimeter-precise 3D algorithms, at a precision that has not been publicly demonstrated. To address this gap, we introduce RoboCap, a 250\,g six-camera dual-IMU hat designed for in-the-wild egocentric data capture, and the Grounded API, a suite of device-agnostic 3D algorithms tuned for RoboCap. In this report, we demonstrate how hardware, calibration, and 3D algorithms interact to achieve state-of-the-art performance on the public benchmarks: our SLAM across diverse settings and rigs, our depth estimation on egocentric settings, and our hand tracking when adapted to third-party devices.

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Qasim Ali, Nikash Bhardwaj, Kheri Hughes, Sam Cho, Ethan Lee, Paul Luo, Shinn Chong, Sunny Yu, Owen Akhibi, Alex Proshkin, Haotian Zhan, Niresh Dravin, Vivek Sharma, Jonathan Victor, Amanda Young, Michael Cho, Ademi Adeniji, Vincent Liu. 2026-10-20. RoboCap: A New Platform for Egocentric Robot Learning. https://arxiv.org/abs/2610.07217

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