Robotic Affection -- Opportunities of AI-based haptic interactions to improve social robotic touch through a multi-deep-learning approach
For researchers in human-robot interaction, this paper outlines a conceptual framework to address a known bottleneck in affective touch, but it is a position paper without empirical results, making it incremental.
This position paper identifies the challenge of affective social touch in human-robot interaction and proposes a novel multi-model architecture that decomposes affective touch into specialized subtasks, aiming to overcome the 'haptic uncanny valley' through a peer-to-peer, state-sharing framework with a Sim-to-Real pipeline.
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.