XRZero-G0：インターフェース、品質、比率で器用なロボット操作の最前線を押し広げる
XRZero-G0: Pushing the Frontier of Dexterous Robotic Manipulation with Interfaces, Quality and Ratios
ロボットを使わない人間のデモデータ収集システムXRZero-G0を提案し、閉ループの品質管理とデータ混合比率の最適化により、少量の実機データで大規模なロボットフリーデータと組み合わせて同等性能を達成し、コストを20分の1に削減した。
詳しい要約
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著者: James Wang, Primo Pu, Zephyr Fung, Alex Wang, Sam Wang, Bender Deng, Kevin Wang, Zivid Liu, Chris Pan, Panda Yang, Andy Zhai, Lucy Liang, Shalfun Li, Johnny Sun, Jacky Xu, Will Tian, Kai Yan, Kohler Ye, Scott Li, Qian Wang, Roy Gan, Hao Wang
分類: cs.RO
原文アブストラクト
The acquisition of high-quality, action-aligned demonstration data remains a fundamental bottleneck in scaling foundation models for dexterous robot manipulation. Although robot-free human demonstrations (e.g., the UMI paradigm) offer a scalable alternative to traditional teleoperation, current systems are constrained by sub-optimal hardware ergonomics, open-loop workflows, and a lack of systematic data-mixing strategies. To address these limitations, we present XRZero-G0, a hardware-software co-designed system for embodied data collection and policy learning. The system features an ergonomic, virtual reality interface equipped with a top-view camera and dual specialized grippers to directly improve collection efficiency. To ensure dataset reliability, we propose a closed-loop collection, inspection, training, and evaluation pipeline for non-proprioceptive data. This workflow achieves an 85% data validity rate and establishes a transparent mechanism for quality control. Furthermore, we investigate the empirical scaling behaviors and optimal mixing ratios of robot-free data. Extensive experiments indicate that combining a minimal volume of real-robot data with large-scale robot-free data (e.g., a 10:1 ratio) achieves performance comparable to exclusively real-robot datasets, while reducing acquisition costs by a factor of twenty. Utilizing XRZero-G0, we construct a 2,000-hour robot-free dataset that enables zero-shot cross-embodiment transfer to a target physical robot, demonstrating a highly scalable methodology for generalized real-world manipulation.Our project repository: https://github.com/X-Square-Robot/XRZero-G0
関連論文
- 粗密模倣学習のための触覚フィードバック付き視覚触覚データ収集システムデータ収集/模倣学習
