TacClaw-Infinity: 巧みな操作のための大規模視覚・触覚・言語・行動データセット
TacClaw-Infinity: A Large-Scale Vision-Tactile-Language-Action Dataset for Dexterous Manipulation
触覚対応のハンドヘルド型デバイスで世界中から収集した1万時間・100万エピソード超の視覚・触覚・言語・行動データセットを構築し、異なるロボット間での転移を実証した。
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著者: Fucai Zhu, Xidong Zhang, Yichi Zhang, Yipan Wang, Fubo Wang, Zibin Wu, Kaiqi Li, Jianhao Jiao, Shihan Lu, Di Hu, Mengdi Xu, Siyu Zhu, Xiaoxiao Long, Yao Yao, Xun Cao, Zhuo Xu, Koichi Hashimoto, Qifeng Chen, Zhe Li, Jianfei Yang, Haofei Lu, Danica Kragic, Shuicheng Yan, Jianghua Duan, Michael Yu Wang, Weihao Yuan
分類: cs.RO
原文アブストラクト
Large-scale robot learning increasingly relies on diverse human demonstrations, yet tactile manipulation data remain difficult to scale, and existing multimodal datasets often lack consistent quality control and structured supervision. We present TacClaw-Infinity, a large-scale structured Vision-Tactile-Language-Action (VTLA) dataset for contact-rich manipulation, collected with TacClaw, a tactile-enabled UMI-style handheld interface that records synchronized visual observations, fingertip tactile signals, and end-effector trajectories without requiring a target robot during data collection. TacClaw-Infinity is built through a distributed worldwide collection across homes, laboratories, industrial workplaces, retail stores, and outdoor environments including tasks contributed by academic and industrial collaborators. Together, these efforts yield over 10,000 hours, 1 million episodes, and 1 billion frames spanning more than 1,000 task types. We develop a scalable pipeline for trajectory reconstruction, multimodal quality control, embodiment-aware filtering, and structured annotation, covering contact-centric segmentation, subtask description, and spatial grounding. We validate TacClaw-Infinity at both the data and model levels: At the data level, we assess the collection interface, the action-space compatibility of the curated demonstrations, and the structured annotations, while for the model level, we demonstrate cross-embodiment transfer across three heterogeneous robots without robot-specific demonstrations.