Touched by ChatGPT: Using an LLM to Drive Affective Tactile Interaction

TL;DR

This paper demonstrates how large language models can generate tactile vibration patterns to convey emotions and gestures through a wearable device, enhancing human-robot emotional communication.

Contribution

It introduces a novel method using LLMs to create expressive tactile signals for robots, enabling nuanced emotional interactions beyond simple gestures.

Findings

Participants accurately recognized the conveyed emotions.

LLMs effectively generated diverse tactile vibration patterns.

The approach enhances emotional expressiveness in human-robot interaction.

Abstract

Touch is a fundamental aspect of emotion-rich communication, playing a vital role in human interaction and offering significant potential in human-robot interaction. Previous research has demonstrated that a sparse representation of human touch can effectively convey social tactile signals. However, advances in human-robot tactile interaction remain limited, as many humanoid robots possess simplistic capabilities, such as only opening and closing their hands, restricting nuanced tactile expressions. In this study, we explore how a robot can use sparse representations of tactile vibrations to convey emotions to a person. To achieve this, we developed a wearable sleeve integrated with a 5x5 grid of vibration motors, enabling the robot to communicate diverse tactile emotions and gestures. Using chain prompts within a Large Language Model (LLM), we generated distinct 10-second vibration patterns corresponding to 10 emotions (e.g., happiness, sadness, fear) and 6 touch gestures (e.g., pat, rub, tap). Participants (N = 32) then rated each vibration stimulus based on perceived valence and arousal. People are accurate at recognising intended emotions, a result which aligns with earlier findings. These results highlight the LLM's ability to generate emotional haptic data and effectively convey emotions through tactile signals. By translating complex emotional and tactile expressions into vibratory patterns, this research demonstrates how LLMs can enhance physical interaction between humans and robots.