Towards Embedding Dynamic Personas in Interactive Robots: Masquerading Animated Social Kinematics (MASK)
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Abstract (arXiv)
This paper presents the design and development of an innovative interactive robotic system to enhance audience engagement using character-like personas. Built upon the foundations of persona-driven dialog agents, this work extends the agent's application to the physical realm, employing robots to provide a more captivating and interactive experience. The proposed system, named the Masquerading Animated Social Kinematic (MASK), leverages an anthropomorphic robot which interacts with guests using non-verbal interactions, including facial expressions and gestures. A behavior generation system based upon a finite-state machine structure effectively conditions robotic behavior to convey distinct personas. The MASK framework integrates a perception engine, a behavior selection engine, and a comprehensive action library to enable real-time, dynamic interactions with minimal human intervention in behavior design. Throughout the user subject studies, we examined whether the users could recognize the intended character in both personality- and film-character-based persona conditions. We conclude by discussing the role of personas in interactive agents and the factors to consider for creating an engaging user experience.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 27 references that are in the map. First posted on arXiv 15 Mar 2024.
Builds on
7 of 27 references.
References · 7
- 2023GPT-4 Technical Reportcited by 18,132
- 2023Character-LLM: A Trainable Agent for Role-Playingcited by 335
- 2023Personality Traits in Large Language Modelscited by 166
- 2019ALOHA: Artificial Learning of Human Attributes for Dialogue Agentscited by 16
- 2023Developing Social Robots with Empathetic Non-Verbal Cues Using Large Language Modelscited by 12
- 2023Come Closer: The Effects of Robot Personality on Human Proxemics Behaviourscited by 2
- 2022Nonverbal Social Behavior Generation for Social Robots Using End-to-End Learningcited by 1
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.