The Potential of Haptic Foundation Models

2026-08-22

AI Summary

研究通过探索触觉基础模型（HFMs）解决现有触觉模型任务特定性强的问题，从四个核心维度进行范式转变，并在TacBench上评估几种大规模触觉数据集。

Abstract

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.

Problem

• Haptic Foundation Models
• embodied AI
• touch sensing
• hardware heterogeneity
• physical interaction

Innovation

• Haptic Foundation Models
• Action Coupling
• Physical Dynamical Representation Space
• Continuous Time-series Data