# HapticWAM: Distilling Imagined Touch into a World-Action Model without Inference-Time Tactile Sensing

arXiv: https://arxiv.org/abs/2609.23888 · HTML: https://mlsanity.com/paper/2609.23888/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 39 references that are in the map.
- First posted on arXiv 20 Sep 2026.

## Placed in

- World models

## Builds on (most cited first, 29 of 29)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Cosmos World Foundation Model Platform for Physical AI](https://mlsanity.com/paper/2501.03575/) (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)
- [Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation](https://mlsanity.com/paper/2602.12125/) (2026)
- [Being-H0.7: A Latent World-Action Model from Egocentric Videos](https://mlsanity.com/paper/2605.00078/) (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)
- [Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation](https://mlsanity.com/paper/2606.08737/) (2026)
- [Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation](https://mlsanity.com/paper/2512.23864/) (2025)
- [World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems](https://mlsanity.com/paper/2604.14732/) (2026)
- [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)
- [HapticVLA: Contact-Rich Manipulation via Vision-Language-Action Model without Inference-Time Tactile Sensing](https://mlsanity.com/paper/2603.15257/) (2026)
- [Tactile-WAM: Touch-Aware World Action Model with Tactile Asymmetric Attention](https://mlsanity.com/paper/2606.26663/) (2026)
- [FAWAM: Force-Aware World Action Models for Closed-Loop Contact-Rich Manipulation](https://mlsanity.com/paper/2606.08555/) (2026)
- [N0-VTLA: Scaling Vision-Tactile-Language-Action Model with Latent Tactile Tokens](https://mlsanity.com/paper/2607.23782/) (2026)
- [PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation](https://mlsanity.com/paper/2603.04531/) (2026)
- [Towards Predictive, Aligned, and Scalable Robot Learning](https://mlsanity.com/paper/2607.11270/) (2026)
- [Echo: An Open-Source, Low-Cost Teleoperation System with Force Feedback for Dataset Collection in Robot Learning](https://mlsanity.com/paper/2504.07939/) (2025)
- [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)
- [TacPAC: Tactile Prediction and Real-Time Action Correction in World-Action Models for Contact-Rich Manipulation](https://mlsanity.com/paper/2609.05266/) (2026)
- [FlowTouch: View-Invariant Visuo-Tactile Prediction](https://mlsanity.com/paper/2603.08255/) (2026)
- [Imagining the Sense of Touch: Touch-Informed Manipulation via Imagined Tactile Representations](https://mlsanity.com/paper/2607.01684/) (2026)

## Abstract (arXiv)

Contact-rich manipulation requires estimating forces, slip and contact geometry that can remain ambiguous in scene images. Optical tactile sensors provide both visual observations of the contact surface and mechanical measurements, yet learning from these signals raises two challenges: representing contact beyond appearance and transferring its benefits to a policy that does not require fingertip observations at deployment. We introduce HapticWAM, a world-action model that combines heterogeneous tactile encoding, structured contact prediction and teacher-student distillation. Its teacher encodes gel images together with deformation, shear, distributed forces, resultant wrench and derived contact state into a frozen video backbone. Rather than predicting tactile pixels alone, the model jointly generates actions and a contact package describing future events and mechanics. Anticipatory Contact Coupling uses the previously imagined package to condition attention, preserving a contact-related input when direct tactile observations are unavailable. Haptic-Imagination Distillation transfers both contact futures and action predictions to a student that retains the generative contact head but removes its fingertip input branches. On a real-world setup, across three contact-rich pick-and-place tasks, HapticWAM Student achieves a 77% per-task mean success rate (41 of 50 starts, 82% pooled), reaching 95% on one of the tasks, outperforming the evaluated teacher and baseline configurations.