# TacWAM: Anchor-Guided World Action Model with Mechanics-Aware Tactile Prediction

arXiv: https://arxiv.org/abs/2607.28391 · HTML: https://mlsanity.com/paper/2607.28391/

## Key facts

- Cited by 3 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 24 of its 29 references that are in the map.
- First posted on arXiv 30 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, 24 of 24)

- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Fast-WAM: Do World Action Models Need Test-time Future Imagination?](https://mlsanity.com/paper/2603.16666/) (2026)
- [Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.02881/) (2025)
- [Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks](https://mlsanity.com/paper/1907.13098/) (2019)
- [VTLA: Vision-Tactile-Language-Action Model with Preference Learning for Insertion Manipulation](https://mlsanity.com/paper/2505.09577/) (2025)
- [AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing](https://mlsanity.com/paper/2606.09811/) (2026)
- [ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation](https://mlsanity.com/paper/2506.15953/) (2025)
- [VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback](https://mlsanity.com/paper/2507.17294/) (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)
- [OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation](https://mlsanity.com/paper/2605.06481/) (2026)
- [RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing](https://mlsanity.com/paper/2407.01418/) (2024)
- [Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization](https://mlsanity.com/paper/2507.09160/) (2025)
- [Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation](https://mlsanity.com/paper/2512.23864/) (2025)
- [TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2606.11184/) (2026)
- [VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2607.02503/) (2026)
- [Tactile-WAM: Touch-Aware World Action Model with Tactile Asymmetric Attention](https://mlsanity.com/paper/2606.26663/) (2026)
- [Tac-Man: Tactile-Informed Prior-Free Manipulation of Articulated Objects](https://mlsanity.com/paper/2403.01694/) (2024)
- [TouchWorld: A Predictive and Reactive Tactile Foundation Model for Dexterous Manipulation](https://mlsanity.com/paper/2607.07287/) (2026)
- [Enhancing Policy Learning with World-Action Model](https://mlsanity.com/paper/2603.28955/) (2026)
- [N0-VTLA: Scaling Vision-Tactile-Language-Action Model with Latent Tactile Tokens](https://mlsanity.com/paper/2607.23782/) (2026)
- [ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation](https://mlsanity.com/paper/2607.22530/) (2026)
- [Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms](https://mlsanity.com/paper/2605.17336/) (2026)
- [τ: Learning Touch-Augmented Vision-Language-Action Models from Future Visual Supervision](https://mlsanity.com/paper/2607.24485/) (2026)

## Built on by (most cited first, 3 of 3)

- [Beyond Multimodal Alignment: Certifying Physical Language through Response Substitution and Ordered Execution](https://mlsanity.com/paper/2608.19492/) (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)

World Action Models (WAMs) combine future-state prediction with robot action generation, but existing approaches largely rely on visual futures. Visual prediction captures scene structure and object motion, yet provides limited supervision for force, deformation, shear, and slip during contact-rich manipulation. This creates two design requirements: tactile futures should carry meaningful physical information, and they should not become privileged cues for action generation. We present TacWAM, a mechanics-aware tactile WAM that addresses this challenge in three steps. First, a Spatially Aligned Fusion (SAF) Tactile Encoder maps tactile appearance, dense force fields, and deformation flow into a shared latent prediction space, with bilateral force and torque reconstruction preserving global contact information. Second, a tactile history encoder provides temporal context so future tactile prediction reflects how force and deformation change beyond the current tactile observation. Third, Anchor-Guided Tri-Modal (AGT) Attention separates current visual and tactile anchors, future prediction tokens, and action tokens, allowing future tactile states to supervise training without being directly read by the action branch. We evaluate TacWAM on four real-world contact-rich manipulation tasks covering fragile grasping, sustained surface contact, and dynamic in-hand manipulation. TacWAM achieves an average success rate of 75.0%, exceeding the strongest evaluated baseline by 37.5 percentage points. Staged ablations show consistent degradation when tactile history is removed and access to future prediction targets is relaxed. These results indicate that future tactile supervision can improve contact-aware action learning when combined with informative tactile representations and deployment-consistent information constraints.