aiqbench analysis
This paper proposes Haptic Foundation Models (HFMs) to overcome the current bottleneck in embodied AI, which lacks generalized touch sensing capabilities. By transitioning from passive language and vision models to active haptic systems, this research aims to enable safer, adaptive physical interactions across diverse consumer devices like wearables and home robots. The study benchmarks existing tactile datasets against key metrics such as force estimation and slip detection to define the trajectory for next-generation embodied intelligence.
The Potential of Haptic Foundation Models
arXiv:2608.28664v1 Announce Type: new 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.
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.
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
arXiv:2608.28664 [cs.RO]
(or arXiv:2608.28664v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.28664 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1109/MCE.2026.3723054 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 260 words
