# τ: Learning Touch-Augmented Vision-Language-Action Models from Future Visual Supervision

arXiv: https://arxiv.org/abs/2607.24485 · HTML: https://mlsanity.com/paper/2607.24485/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Tactile, force-aware and humanoid VLA, one of 13 directions in Vision-language-action models.
- Builds on 34 of its 38 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Placed in

- Vision-language-action models / [Tactile, force-aware and humanoid VLA](https://mlsanity.com/vla/tactile-force-aware-and-humanoid-vla/)

## Builds on (most cited first, 30 of 34)

- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [Zero-Shot Learning Through Cross-Modal Transfer](https://mlsanity.com/paper/1301.3666/) (2013)
- [Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.02881/) (2025)
- [Binding Touch to Everything: Learning Unified Multimodal Tactile Representations](https://mlsanity.com/paper/2401.18084/) (2024)
- [ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation](https://mlsanity.com/paper/2505.22159/) (2025)
- [Touch and Go: Learning from Human-Collected Vision and Touch](https://mlsanity.com/paper/2211.12498/) (2022)
- [Ambient Sound Provides Supervision for Visual Learning](https://mlsanity.com/paper/1608.07017/) (2016)
- [VTLA: Vision-Tactile-Language-Action Model with Preference Learning for Insertion Manipulation](https://mlsanity.com/paper/2505.09577/) (2025)
- [AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors](https://mlsanity.com/paper/2502.12191/) (2025)
- [Self-Supervised Visuo-Tactile Pretraining to Locate and Follow Garment Features](https://mlsanity.com/paper/2209.13042/) (2022)
- [OmniVTLA: Vision-Tactile-Language-Action Models with Semantic-Aligned Tactile Sensing](https://mlsanity.com/paper/2508.08706/) (2025)
- [Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper](https://mlsanity.com/paper/2507.15062/) (2025)
- [Touch100k: A Large-Scale Touch-Language-Vision Dataset for Touch-Centric Multimodal Representation](https://mlsanity.com/paper/2406.03813/) (2024)
- [MLA: A Multisensory Language-Action Model for Multimodal Understanding and Forecasting in Robotic Manipulation](https://mlsanity.com/paper/2509.26642/) (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)
- [VTAM: Video-Tactile-Action Models for Complex Physical Interaction Beyond VLAs](https://mlsanity.com/paper/2603.23481/) (2026)
- [Learning rich touch representations through cross-modal self-supervision](https://mlsanity.com/paper/2101.08616/) (2021)
- [TaF-VLA: Tactile-Force Alignment in Vision-Language-Action Models for Force-aware Manipulation](https://mlsanity.com/paper/2601.20321/) (2026)
- [FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation](https://mlsanity.com/paper/2606.13102/) (2026)
- [T-Rex: Tactile-Reactive Dexterous Manipulation](https://mlsanity.com/paper/2606.17055/) (2026)
- [TouchGuide: Inference-Time Steering of Visuomotor Policies via Touch Guidance](https://mlsanity.com/paper/2601.20239/) (2026)

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

- [TacWAM: Anchor-Guided World Action Model with Mechanics-Aware Tactile Prediction](https://mlsanity.com/paper/2607.28391/) (2026)
- [Self-Supervised Anchoring of Fingertip Sensing to Proprioception and Proactive Actions for Robot Imitation Learning](https://mlsanity.com/paper/2609.29822/) (2026)

## Abstract (arXiv)

Incorporating tactile sensing into Vision-Language-Action (VLA) models holds promise for contact-rich manipulation, where visual observations alone often fail to capture critical cues about physical interactions. However, learning informative tactile representation while effectively adapting it to pretrained VLA models remains challenging under limited task-specific data. Existing methods either focus on instantaneous contact states or model temporal interaction dynamics using 6D wrench sequences, leaving high-dimensional tactile signals underexplored. To address these challenges, we present τ, a touch-augmented VLA framework that learns an action-conditioned spatiotemporal tactile representation from future visual supervision inspired by the Joint-Embedding Predictive Architecture (JEPA), and fuses it with vision-language features for action generation. This supervision operates in latent space and is used only during training, adding no deployment overhead. We also introduce TacAura, a dataset of synchronized vision, proprioception, and vision-based tactile signals across four representative contact-rich manipulation tasks. Experiments show that τ outperforms existing models and generalizes to unseen objects and scenes, delivering improved manipulation performance and robustness. Project Page: https://cocacola-lab.github.io/tau-Page/.