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

arXiv cs.RO / 5/5/2026

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Key Points

The paper argues that while robots have improved grasping and dexterity with haptic feedback, affective social touch (e.g., handshakes or reassuring strokes) is still a major unsolved challenge in human-robot interaction.
It proposes a novel multi-model architecture that breaks affective touch into specialized subtasks, inspired by neurobiology and targeting limitations across AI, haptics, and robotics.
The authors frame affective touch as a distributed, closed-loop perceptual task rather than a single monolithic motor action to help reduce the “haptic uncanny valley.”
The approach uses a peer-to-peer, state-sharing framework intended to enable scalable, cumulative development in a Sim-to-Real pipeline and support interdisciplinary collaboration.
Overall, the work outlines a pathway toward a unified, expressive system for social robotics that multiple research groups can contribute to independently while staying coherent.

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.