[
    {
        "id": "osp-15772",
        "type": "article-journal",
        "title": "RoboCap: A New Platform for Egocentric Robot Learning",
        "author": [
            {
                "family": "Superintelligence",
                "given": "Grounded"
            },
            {
                "family": "BitRobot",
                "given": ""
            }
        ],
        "URL": "https://omanscience.com/ar/articles/robocap-a-new-platform-for-egocentric-robot-learning",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]