SOURCE-LINKED INTELLIGENCE
TacSushi: Tactile-Grounded World-Action Modeling for Dexterous Sushi Manipulation
Dexterous food manipulation requires control under deformation, occlusion, and uncertain contact. We present TacSushi, a tactile-grounded, Cosmos3-based world-action policy that learns from recorded future consequences while acting on current observations. The backbone encodes current RGB, language, and hand state, and feature-wise gated fusion incorporates fingertip tactile features into the action representation. During training, a decoder conditioned on demonstrated action chunks predicts logged future visual observations, task progress, relative contact risk, and tactile summaries; this de
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-17T03:01:35.000Z
- arXiv · Artificial Intelligence · 2026-09-17T03:01:35.000Z
First collected: 2026-09-19T20:26:32.566Z. This is not the publication date.