Xpress: A System For Dynamic, Context-Aware Robot Facial Expressions using Language Models
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| author | Antony, Victor Nikhil Stiber, Maia Huang, Chien-Ming |
| author_facet | Antony, Victor Nikhil Stiber, Maia Huang, Chien-Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00283 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Xpress: A System For Dynamic, Context-Aware Robot Facial Expressions using Language Models Antony, Victor Nikhil Stiber, Maia Huang, Chien-Ming Robotics Human-Computer Interaction 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. |
| title | Xpress: A System For Dynamic, Context-Aware Robot Facial Expressions using Language Models |
| topic | Robotics Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.00283 |