VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation

• URL: http://arxiv.org/abs/2607.02503v1
• Date: Thu, 02 Jul 2026 17:58:36 GMT
• Title: VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation
• Abstract summary: Contact-rich manipulation requires policies to react to local deformation, pressure, slip, and friction.<n>Existing visual-tactile policies usually feed tactile observations directly into action prediction.<n>We introduce VT-WAM, a Visual-Tactile World Action Model that jointly learns future visual prediction, tactile deformation prediction, and action prediction.
• Score: 22.0330577788623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contact-rich manipulation requires policies to react to local deformation, pressure, slip, and friction, yet these cues are temporally sparse and often invisible in visual observations. Existing visual-tactile policies usually feed tactile observations directly into action prediction, but rarely model tactile deformation dynamics during action generation. In this paper, we introduce VT-WAM, a Visual-Tactile World Action Model that jointly learns future visual prediction, tactile deformation prediction, and action prediction within a unified flow matching framework. In particular, VT-WAM introduces (1) Asymmetric Mixture-of-Transformers (MoT) attention to bridge a first-frame visual anchor with temporal tactile dynamics, and (2) contact-gated Action-Visual-Tactile Attention Guidance (AVTAG) to encourage action queries to rely on tactile evidence during contact phases. Across six real-world contact-rich manipulation tasks, VT-WAM achieves a 71.67% average success rate, outperforming Fast-WAM by 26.67% and OmniVTLA by 35.84%. Ablations demonstrate that modeling tactile deformation dynamics and guiding contact-phase tactile attention are both important for contact-rich tasks. Project website: https://vt-wam.github.io/.

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