# The Potential of Haptic Foundation Models

arXiv: https://arxiv.org/abs/2608.28664 · HTML: https://mlsanity.com/paper/2608.28664/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 23 Aug 2026.

## Builds on (most cited first, 10 of 10)

- [Binding Touch to Everything: Learning Unified Multimodal Tactile Representations](https://mlsanity.com/paper/2401.18084/) (2024)
- [AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors](https://mlsanity.com/paper/2502.12191/) (2025)
- [ReSkin: versatile, replaceable, lasting tactile skins](https://mlsanity.com/paper/2111.00071/) (2021)
- [Touch100k: A Large-Scale Touch-Language-Vision Dataset for Touch-Centric Multimodal Representation](https://mlsanity.com/paper/2406.03813/) (2024)
- [FreeTacMan: Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation](https://mlsanity.com/paper/2506.01941/) (2025)
- [exUMI: Extensible Robot Teaching System with Action-aware Task-agnostic Tactile Representation](https://mlsanity.com/paper/2509.14688/) (2025)
- [AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception](https://mlsanity.com/paper/2602.09617/) (2026)
- [Tactile Beyond Pixels: Multisensory Touch Representations for Robot Manipulation](https://mlsanity.com/paper/2506.14754/) (2025)
- [World In Your Hands: A Large-Scale and Open-Source Ecosystem for Learning Human-Centric Manipulation in the Wild](https://mlsanity.com/paper/2512.24310/) (2025)
- [Bringing Robots Home: The Rise of AI Robots in Consumer Electronics](https://mlsanity.com/paper/2403.14449/) (2024)

## Abstract (arXiv)

Despite the success of foundation models in language and vision, their expansion into embodied AI is bottlenecked by a lack of generalized touch sensing. This limitation is especially relevant to consumer electronics, where smartphones, wearables, VR controllers, home robots, and health monitoring devices require safe and adaptive physical interaction. Constrained by hardware heterogeneity and the necessity of active physical data collection, current haptic models remain rigidly task-specific. To overcome these limitations, this article explores the transformative potential and developmental trajectory of Haptic Foundation Models (HFMs). We detail the paradigm shift required to transition from passive Large Language Models and Vision Language Models into active HFMs across four core dimensions: action coupling, physical dynamical representation space, continuous time-series data granularity, and action-conditioned future state prediction. Furthermore, we synthesize existing large-scale tactile datasets and benchmark UniTouch, AnyTouch, T3, and Sparsh on TacBench for force estimation, slip detection, and relative pose estimation.