STAR: 視覚・触覚・言語・行動モデルにおけるスパース触覚表現学習による巧みなマニピュレーション
STAR: Sparse Tactile Representation Learning in Vision-Tactile-Language-Action Models for Dexterous Manipulation
200時間の双腕巧みな操作データセットを構築し、触覚信号のスパース性に対処する視覚・触覚・言語・行動モデルSTARを提案。実世界4タスクで平均61%の成功率を達成した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Xiangcheng Liu, Tianhao Wu, Le Zheng, Yidong Wang, Bowen Jiang, Mingjie Pan, Xinlin Ren, Yi Liu, Jianlan Luo
分類: cs.RO
原文アブストラクト
Dexterous manipulation requires coordinated multi-finger control and effective tactile feedback, yet learning these capabilities remains challenging due to the lack of large-scale real-world data and the difficulty of extracting effective representations from sparse tactile signals. We build a robot platform and teleoperation system to collect a 200-hour bimanual dexterous manipulation dataset with synchronized visual, tactile, and language annotations, comprising 10,576 trajectories across 65 tasks, 69.5% of which involve dexterous multi-finger manipulation. We further propose STAR, an integrated training recipe for vision-tactile-language-action (VTLA) models that addresses the spatial, temporal, and informational sparsity of tactile signals through visual-tactile joint pre-training, sparse-global tactile token representation, and sparse future tactile prediction. Trained on this dataset, STAR achieves a 61% average success rate across four real-world tasks with 100 post-training trajectories per task, demonstrating dexterous performance under task-specific post-training.