ArticleFrontiers in robotics and AI2023
Real-time emotion generation in human-robot dialogue using large language models.
Article in Frontiers in robotics and AI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Self-experienced empathetic behaviour patterns in medical students during virtual patient encounters: a comparison between an AI-enhanced social robot and a computer-based platform.Frontiers in artificial intelligence · 2026Article
- Persuasion, pride and prejudice: factors associated with students' resistance to a social robot's incorrect mathematical solution.Frontiers in robotics and AI · 2026Article
- Virtual Patient Simulations Using Social Robotics Combined With Large Language Models for Clinical Reasoning Training in Medical Education: Mixed Methods Study.Journal of medical Internet research · 2025Article
- Research progress and implications of the application of large language model in shared decision-making in China's healthcare field.Frontiers in public health · 2025Review
- Exploring LLM-powered multi-session human-robot interactions with university students.Frontiers in robotics and AI · 2025Article
- Evaluating human perceptions of android robot facial expressions based on variations in instruction styles.Frontiers in robotics and AI · 2025Article
- Do you feel like (A)I feel?Frontiers in psychology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Affective behaviors enable social robots to not only establish better connections with humans but also serve as a tool for the robots to express their internal states. It has been well established that emotions are important to signal understanding in Human-Robot Interaction (HRI). This work aims to harness the power of Large Language Models (LLM) and proposes an approach to control the affective behavior of robots. By interpreting emotion appraisal as an Emotion Recognition in Conversation (ERC) tasks, we used GPT-3.5 to predict the emotion of a robot's turn in real-time, using the dialogue history of the ongoing conversation. The robot signaled the predicted emotion using facial expressions. The model was evaluated in a within-subjects user study (
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