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

arXiv: https://arxiv.org/abs/2607.02503 · HTML: https://mlsanity.com/paper/2607.02503/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and robot world models, one of 11 directions in World models.
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 2 Jul 2026.

## Placed in

- World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/)

## Builds on (most cited first, 18 of 18)

- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [World Action Models are Zero-shot Policies](https://mlsanity.com/paper/2602.15922/) (2026)
- [Fast-WAM: Do World Action Models Need Test-time Future Imagination?](https://mlsanity.com/paper/2603.16666/) (2026)
- [Motus: A Unified Latent Action World Model](https://mlsanity.com/paper/2512.13030/) (2025)
- [Unified Video Action Model](https://mlsanity.com/paper/2503.00200/) (2025)
- [GigaWorld-Policy: An Efficient Action-Centered World--Action Model](https://mlsanity.com/paper/2603.17240/) (2026)
- [VTLA: Vision-Tactile-Language-Action Model with Preference Learning for Insertion Manipulation](https://mlsanity.com/paper/2505.09577/) (2025)
- [World Action Models: The Next Frontier in Embodied AI](https://mlsanity.com/paper/2605.12090/) (2026)
- [OmniVTLA: Vision-Tactile-Language-Action Models with Semantic-Aligned Tactile Sensing](https://mlsanity.com/paper/2508.08706/) (2025)
- [OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation](https://mlsanity.com/paper/2603.19201/) (2026)
- [Visuo-Tactile World Models](https://mlsanity.com/paper/2602.06001/) (2026)
- [Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization](https://mlsanity.com/paper/2507.09160/) (2025)
- [TaF-VLA: Tactile-Force Alignment in Vision-Language-Action Models for Force-aware Manipulation](https://mlsanity.com/paper/2601.20321/) (2026)
- [Tactile-Conditioned Diffusion Policy for Force-Aware Robotic Manipulation](https://mlsanity.com/paper/2510.13324/) (2025)
- [Say, Dream, and Act: Learning Video World Models for Instruction-Driven Robot Manipulation](https://mlsanity.com/paper/2602.10717/) (2026)
- [KineDex: Learning Tactile-Informed Visuomotor Policies via Kinesthetic Teaching for Dexterous Manipulation](https://mlsanity.com/paper/2505.01974/) (2025)
- [Master Micro Residual Correction with Adaptive Tactile Fusion and Force-Mixed Control for Contact-Rich Manipulation](https://mlsanity.com/paper/2603.15152/) (2026)

## Built on by (most cited first, 11 of 11)

- [BadWAM: When World-Action Models Dream Right but Act Wrong](https://mlsanity.com/paper/2607.15207/) (2026)
- [TacWAM: Anchor-Guided World Action Model with Mechanics-Aware Tactile Prediction](https://mlsanity.com/paper/2607.28391/) (2026)
- [N0-TWAM: Scaling Tactile-Native World-Action Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2607.23783/) (2026)
- [HiTac-WAM: A Hierarchical Tactile World Action Model for Contact-Rich Robot Manipulation](https://mlsanity.com/paper/2608.19574/) (2026)
- [TacForcing: Streaming Action Generation with Execution-Time Tactile Feedback](https://mlsanity.com/paper/2608.25798/) (2026)
- [Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning](https://mlsanity.com/paper/2608.07558/) (2026)
- [Disentangling Visuo-Tactile Foresight: Oracle-Guided Interface Discovery for World Action Models](https://mlsanity.com/paper/2608.00547/) (2026)
- [ME-Dex 1.0: Bringing Heterogeneous Tactile Sensing into World Action Modeling](https://mlsanity.com/paper/2609.21449/) (2026)
- [HapticWAM: Distilling Imagined Touch into a World-Action Model without Inference-Time Tactile Sensing](https://mlsanity.com/paper/2609.23888/) (2026)
- [Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation](https://mlsanity.com/paper/2609.35450/) (2026)
- [TacDyn-WAM: Learning Implicit Tactile Dynamics in a Heterogeneous Visuo-Tactile World Action Model](https://mlsanity.com/paper/2610.00638/) (2026)

## Abstract (arXiv)

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/.