arXiv：cs.AI· Grounded Superintelligence, BitRobot·· 10 小时前AI 评分40
RoboCap：面向第一人称机器人学习的新平台
RoboCap: A New Platform for Egocentric Robot Learning
AI 导读
RoboCap 是一顶 250g、配备六个摄像头和双 IMU 的帽子，用于野外第一人称数据采集，同时配套设备无关的 Grounded API 3D 算法套件。该平台在 SLAM、第一人称深度估计和手部追踪的公开基准上取得 SOTA 表现，手部追踪还可适配第三方设备。
正文
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.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2610.07217 [cs.RO] |
| (or arXiv:2610.07217v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07217 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Vincent Liu [view email]
[v1]
Mon, 5 Oct 2026 18:28:48 UTC (25,745 KB)
来源：arXiv：cs.AI · arxiv.org