# Xpress: A System For Dynamic, Context-Aware Robot Facial Expressions using Language Models

arXiv: https://arxiv.org/abs/2503.00283 · HTML: https://mlsanity.com/paper/2503.00283/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [X2C: A Large-Scale Benchmark for Nuanced Humanoid Facial Expression Imitation](https://mlsanity.com/paper/2505.11146/) (2025)

## Abstract (arXiv)

Facial expressions are vital in human communication and significantly influence outcomes in human-robot interaction (HRI), such as likeability, trust, and companionship. However, current methods for generating robotic facial expressions are often labor-intensive, lack adaptability across contexts and platforms, and have limited expressive ranges--leading to repetitive behaviors that reduce interaction quality, particularly in long-term scenarios. We introduce Xpress, a system that leverages language models (LMs) to dynamically generate context-aware facial expressions for robots through a three-phase process: encoding temporal flow, conditioning expressions on context, and generating facial expression code. We demonstrated Xpress as a proof-of-concept through two user studies (n=15x2) and a case study with children and parents (n=13), in storytelling and conversational scenarios to assess the system's context-awareness, expressiveness, and dynamism. Results demonstrate Xpress's ability to dynamically produce expressive and contextually appropriate facial expressions, highlighting its versatility and potential in HRI applications.