# HiTac-WAM: A Hierarchical Tactile World Action Model for Contact-Rich Robot Manipulation

arXiv: https://arxiv.org/abs/2608.19574 · HTML: https://mlsanity.com/paper/2608.19574/

## Key facts

- Cited by 2 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 13 of its 34 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Placed in

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

## Builds on (most cited first, 13 of 13)

- [World Action Models are Zero-shot Policies](https://mlsanity.com/paper/2602.15922/) (2026)
- [ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation](https://mlsanity.com/paper/2506.15953/) (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)
- [ViTaMIn: Learning Contact-Rich Tasks Through Robot-Free Visuo-Tactile Manipulation Interface](https://mlsanity.com/paper/2504.06156/) (2025)
- [Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation](https://mlsanity.com/paper/2606.08737/) (2026)
- [VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2607.02503/) (2026)
- [Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation through Grounded Anomaly Classification and Recovery Policies](https://mlsanity.com/paper/1809.03979/) (2018)
- [UniTacVLA: Unified Tactile Understanding and Prediction in Vision Language Action Models](https://mlsanity.com/paper/2606.31723/) (2026)
- [Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation](https://mlsanity.com/paper/2607.01067/) (2026)
- [ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation](https://mlsanity.com/paper/2607.22530/) (2026)
- [KDPE: A Kernel Density Estimation Strategy for Diffusion Policy Trajectory Selection](https://mlsanity.com/paper/2508.10511/) (2025)
- [Imagine2touch: Predictive Tactile Sensing for Robotic Manipulation using Efficient Low-Dimensional Signals](https://mlsanity.com/paper/2405.01192/) (2024)

## Built on by (most cited first, 2 of 2)

- [World-Action Models for Robot Learning and Control: A Survey](https://mlsanity.com/paper/2609.16074/) (2026)
- [HapticWAM: Distilling Imagined Touch into a World-Action Model without Inference-Time Tactile Sensing](https://mlsanity.com/paper/2609.23888/) (2026)

## Abstract (arXiv)

World action models jointly predict future visual observations and actions, whereas existing tactile-aware variants typically represent future touch as an image or latent stream without modeling the physical dependencies that organize tactile states hierarchically. We present HiTac-WAM, a hierarchical tactile world action model that forecasts a sequence of future tactile states for each candidate action chunk before execution. The forecast factorizes into contact state, a 3D deformation field, and slip risk, organized as a directed hierarchy in which each downstream stage is conditioned on stop-gradient signals from preceding stages. A directed attention mask allows tactile queries to attend to the video-action context of each candidate while preventing video and action queries from attending to tactile tokens. For planning, HiTac-WAM ranks candidate action chunks using tactile forecasts and task-progress estimates. For execution, the selected tactile forecast is retained as a reference; persistent discrepancies between predicted and observed tactile states trigger corrective replanning. HiTac-WAM achieves a mean contact F1 of 0.921; under matched training budgets, the directed hierarchy reduces 3D displacement L2 error by 17.6% relative to the deformation-only predictor and improves slip AUPRC by 60.4% relative to the slip-only predictor. Across chip grasping, blackboard erasing, and USB insertion, selection guided by the hierarchical forecasts increases the average real-robot success rate from 31.1% to 61.1%, while the full system attains 72.2%.