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

TL;DR

This paper discusses a novel multi-model AI architecture inspired by neurobiology to enhance social robotic touch, addressing current limitations in affective haptic interactions through a distributed, closed-loop approach.

Contribution

It introduces a multi-model, neurobiologically inspired framework for affective social touch, enabling scalable, interdisciplinary development in human-robot interaction.

Findings

Proposes a multi-model architecture for affective touch

Addresses the 'haptic uncanny valley' challenge

Supports scalable development in a Sim-to-Real pipeline

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.