Exploring LLM-generated Culture-specific Affective Human-Robot Tactile Interaction

TL;DR

This study explores how large language models can generate culturally sensitive tactile behaviors for human-robot interaction, revealing insights into emotional decoding, cultural effects, and social appropriateness.

Contribution

It demonstrates the potential of LLMs to produce culturally adaptive tactile behaviors for emotional communication in robots, highlighting cultural and interaction role influences.

Findings

Participants successfully decoded 6 out of 12 emotions under matched cultural conditions.

Tactile behaviors were rated more appropriate from human to robot than from robot to human.

Cultural mismatches decreased decoding accuracy and increased perceived inappropriateness.

Abstract

As large language models (LLMs) become increasingly integrated into robotic systems, their potential to generate socially and culturally appropriate affective touch remains largely unexplored. This study investigates whether LLMs-specifically GPT-3.5, GPT-4, and GPT-4o --can generate culturally adaptive tactile behaviours to convey emotions in human-robot interaction. We produced text based touch descriptions for 12 distinct emotions across three cultural contexts (Chinese, Belgian, and unspecified), and examined their interpretability in both robot-to-human and human-to-robot scenarios. A total of 90 participants (36 Chinese, 36 Belgian, and 18 culturally unspecified) evaluated these LLM-generated tactile behaviours for emotional decoding and perceived appropriateness. Results reveal that: (1) under matched cultural conditions, participants successfully decoded six out of twelve emotions-mainly socially oriented emotions such as love and Ekman emotions such as anger, however, self-focused emotions like pride and embarrassment were more difficult to interpret; (2) tactile behaviours were perceived as more appropriate when directed from human to robot than from robot to human, revealing an asymmetry in social expectations based on interaction roles; (3) behaviours interpreted as aggressive (e.g., anger), overly intimate (e.g., love), or emotionally ambiguous (i.e., not clearly decodable) were significantly more likely to be rated as inappropriate; and (4) cultural mismatches reduced decoding accuracy and increased the likelihood of behaviours being judged as inappropriate.