EMOTION: Expressive Motion Sequence Generation for Humanoid Robots with In-Context Learning

Understand

This paper introduces a framework, called EMOTION, for generating expressive motion sequences in humanoid robots, enhancing their ability to engage in humanlike non-verbal communication.

• Non-verbal cues such as facial expressions, gestures, and body movements play a crucial role in effective interpersonal interactions.
• Despite the advancements in robotic behaviors, existing methods often fall short in mimicking the diversity and subtlety of human non-verbal communication.
• To address this gap, our approach leverages the in-context learning capability of large language models (LLMs) to dynamically generate socially appropriate gesture motion sequences for human-robot interaction.