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World In Your Hands: A Large-Scale and Open-source Ecosystem for Learning Human-centric Manipulation in the Wild

TARS Robotics
Yupeng Zheng
Jichao Peng
Weize Li
Yuhang Zheng
Xiang Li
Yujie Jin
Julong Wei
Guanhua Zhang
Ruiling Zheng
Ming Cao
Songen Gu
Zhenhong Zou
Kaige Li
Ke Wu
Mingmin Yang
Jiahao Liu
Pengfei Li
Hengjie Si
Feiyu Zhu
Wang Fu
Likun Wang
Ruiwen Yao
Jieru Zhao
Yilun Chen
Wenchao Ding
Main:8 Pages
18 Figures
Bibliography:3 Pages
6 Tables
Appendix:9 Pages
Abstract

Large-scale pre-training is fundamental for generalization in language and vision models, but data for dexterous hand manipulation remains limited in scale and diversity, hindering policy generalization. Limited scenario diversity, misaligned modalities, and insufficient benchmarking constrain current human manipulation datasets. To address these gaps, we introduce World In Your Hands (WiYH), a large-scale open-source ecosystem for human-centric manipulation learning. 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 multi-modal 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 significantly enhances the generalization and robustness of dexterous hand policies in tabletop manipulation tasks. We believe that World In Your Hands will bring new insights into human-centric data collection and policy learning to the community.

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