The field of human-robot interaction (HRI) and conversational AI is rapidly evolving, with a focus on creating more personalized, scalable, and effective interactions between humans and robots. Recent developments have highlighted the importance of contextual adaptability, emotional alignment, and persuasive dialogue in HRI. Researchers are exploring new approaches to improve user engagement, such as using generative social robots, adaptive emotional alignment, and self-clone chatbots. The development of open-source toolkits and frameworks is also facilitating the creation of social robotic avatars and conversational AI systems. Noteworthy papers in this area include: CARIS, which introduces a context-adaptable robot interface system for personalized and scalable HRI. ChatCLIDS, which presents a benchmark for evaluating persuasive dialogue in health behavior change. HumAIne-Chatbot, which demonstrates the effectiveness of AI-driven user profiling in personalized conversational AI.
Advancements in Human-Robot Interaction and Conversational AI
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CARIS: A Context-Adaptable Robot Interface System for Personalized and Scalable Human-Robot Interaction
ChatCLIDS: Simulating Persuasive AI Dialogues to Promote Closed-Loop Insulin Adoption in Type 1 Diabetes Care
A Theoretical Framework of the Processes of Change in Psychotherapy Delivered by Artificial Agents
Chatbot Deployment Considerations for Application-Agnostic Human-Machine Dialogues
The Impact of Adaptive Emotional Alignment on Mental State Attribution and User Empathy in HRI
Exploring persuasive Interactions with generative social robots: An experimental framework
HumAIne-Chatbot: Real-Time Personalized Conversational AI via Reinforcement Learning
SRWToolkit: An Open Source Wizard of Oz Toolkit to Create Social Robotic Avatars
Talking to an AI Mirror: Designing Self-Clone Chatbots for Enhanced Engagement in Digital Mental Health Support
"It was Tragic": Exploring the Impact of a Robot's Shutdown