用多模型框架让机器人更懂情感触摸，突破触觉交互的尴尬区。
Robotic Affection -- Opportunities of AI-based haptic interactions to improve social robotic touch through a multi-deep-learning approach
- 拆解情感触摸为多个子任务，用神经生物学启发的分布式模型处理。
- 通过状态共享的闭环框架，提升触觉反馈的真实感与连贯性。
- 适合研究人机交互、触觉机器人和跨领域AI融合的学者参考。
尽管触觉信息在机器人抓取与灵巧操作方面取得进展，但如握手或安抚抚摸等情感性社交触摸仍是人机交互中的重大挑战。本文综述了人工智能、触觉技术与机器人学研究的现状与局限，并提出一种新型多模型架构以填补这些空白。受神经生物学启发，我们将情感触摸分解为若干独立的专用子任务模型。通过将情感触摸视为分布式、闭环的感知任务而非单一运动行为，我们旨在借助同行间状态共享的框架克服‘触觉恐怖谷’现象。该方法支持从仿真到现实的可扩展、累积式开发，促进触觉、人工智能与机器人学研究者的独立贡献与协同工作，为构建统一且富有表现力的社会机器人系统提供可行路径。
原文摘要 · Abstract (English)
Despite the advancement in robotic grasping and dexterity through haptic information, affective social touch, such as handshaking or reassuring stroking, remains a major challenge in Human-Robot-Interaction. This position paper examines current progress and limitations across artificial intelligence, haptics and robotics research, and proposes a novel multi-model architecture to address these gaps. Drawing inspiration from neurobiology, we decompose affective touch into distinct, specialized subtasks models. By treating affective touch as a distributed, closed-loop perceptual task rather than a monolithic motoric movement, we aim to overcome the "haptic uncanny valley" through a peer-to-peer, state-sharing framework. Our approach supports scalable and cumulative development within a Sim-to-Real pipeline, fostering interdisciplinary collaboration. By enabling haptics, AI, and robotics researchers to contribute independently yet coherently, we outline a pathway toward a unified, expressive system for social robotics.
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