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World In Your Hands represents a significant advancement in robotics by providing a massive, open-source dataset of human manipulation skills collected in real-world environments. This resource directly addresses the data scarcity bottleneck for training AI agents to perform complex tasks like picking and placing objects in cluttered scenes, potentially boosting success rates from single digits to over 60%. By making both the hardware and multimodal data accessible, this ecosystem aims to accelerate cross-embodiment policy learning and bridge the gap between human dexterity and robotic execution.
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World In Your Hands: A Large-Scale and Open-Source Ecosystem for Learning Human-Centric Manipulation in the Wild

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2512.24310v4 Announce Type: replace Abstract: We introduce World In Your Hands (WIYH), a large-scale open-source ecosystem comprising over 1,000 hours of human manipulation data collected in-the-wild with millimeter-scale motion accuracy. Specifically, WIYH includes (1) the Oracle Suite, a wearable data collection kit with an auto-labeling pipeline for accurate motion capture; (2) the WIYH Dataset, featuring over 1,000 hours of multimodal manipulation data across hundreds of skills in diverse real-world scenarios; and (3) extensive annotations and benchmarks supporting tasks from perception to action. Furthermore, experiments based on the WIYH ecosystem show that integrating WIYH's human-centric data improves robotic manipulation success rates from 8% to 60% in cluttered scenes. World In Your Hands provides a foundation for advancing human-centric data collection and cross-embodiment policy learning. All data and hardware design will be open-source.

Abstract:We introduce World In Your Hands (WIYH), a large-scale open-source ecosystem comprising over 1,000 hours of human manipulation data collected in-the-wild with millimeter-scale motion accuracy. Specifically, WIYH includes (1) the Oracle Suite, a wearable data collection kit with an auto-labeling pipeline for accurate motion capture; (2) the WIYH Dataset, featuring over 1,000 hours of multimodal manipulation data across hundreds of skills in diverse real-world scenarios; and (3) extensive annotations and benchmarks supporting tasks from perception to action. Furthermore, experiments based on the WIYH ecosystem show that integrating WIYH's human-centric data improves robotic manipulation success rates from 8% to 60% in cluttered scenes. World In Your Hands provides a foundation for advancing human-centric data collection and cross-embodiment policy learning. All data and hardware design will be open-source.

Robotics (cs.RO)

arXiv:2512.24310 [cs.RO]

(or arXiv:2512.24310v4 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2512.24310 Focus to learn more arXiv-issued DOI via DataCite

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