Robotic Affection -- Opportunities of AI-based haptic interactions to improve social robotic touch through a multi-deep-learning approach

• URL: http://arxiv.org/abs/2605.02538v1
• Date: Mon, 04 May 2026 12:40:46 GMT
• Title: Robotic Affection -- Opportunities of AI-based haptic interactions to improve social robotic touch through a multi-deep-learning approach
• Abstract summary: affective social touch, such as handshaking or reassuring, remains a major challenge in Human-Robot-Interaction.<n>This position paper examines current progress and limitations across artificial intelligence, haptics and robotics research.
• Score: 4.1013146687234405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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