发表机构
University of Ottawa; Dayan Technologies（渥太华大学; 大眼科技）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对具身AI中通用触觉感知的瓶颈，探索触觉基础模型（HFMs）的潜力，阐述其需从被动大模型转变的四个核心维度，并在TacBench上对多个触觉基准开展三类任务评估。
AI 中文摘要
尽管基础模型在语言和视觉领域取得了成功，但它们向具身人工智能的扩展却因缺乏通用触觉感知而受阻。这一限制与消费电子领域尤其相关，其中智能手机、可穿戴设备、VR控制器、家用机器人以及健康监测设备都需要安全且自适应的物理交互。受硬件异质性和主动物理数据采集必要性的限制，当前的触觉模型仍然严格针对特定任务。为克服这些限制，本文探讨了触觉基础模型（Haptic Foundation Models, HFMs）的变革性潜力和发展轨迹。我们详细阐述了从被动式大语言模型和视觉语言模型向主动式HFMs转变所需的范式转变，涉及四个核心维度：动作耦合、物理动力学表征空间、连续时间序列数据粒度以及动作条件下的未来状态预测。此外，我们整合了现有的大规模触觉数据集，并在TacBench上对UniTouch、AnyTouch、T3和Sparsh基准进行了力估计、滑动检测和相对位姿估计。
英文摘要
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.
Commentsaccepted by IEEE Consumer Electronics Magazine
