STAR: Sparse Tactile Representation Learning in Vision-Tactile-Language-Action Models for Dexterous Manipulation

• URL: http://arxiv.org/abs/2609.12549v1
• Date: Fri, 11 Sep 2026 07:57:24 GMT
• Title: STAR: Sparse Tactile Representation Learning in Vision-Tactile-Language-Action Models for Dexterous Manipulation
• Abstract summary: Dexterous manipulation requires coordinated multi-finger control and effective tactile feedback.<n>We build a robot platform and teleoperation system to collect a 200-hour bimanual dexterous manipulation dataset.<n>We propose STAR, an integrated training recipe for vision-tactile-language-action models.
• Score: 24.489844571043808
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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