Intelligent Embodiment: Social Robots that Understand and Adapt Through Recommender Systems

ongoing

Social robots, conceived to provide companionship, emotional support, and assistance with daily tasks, play a crucial role in promoting mental health and wellbeing, aligning with the United Nations Sustainable Development Goals. Despite advancements in social robots’ ability to understand and respond to humans, they have limited capabilities in personalization and adaptivity, which are crucial for ensuring long-term user engagement. Meanwhile, given the rapid growth of the AI-driven e-commerce market and the widespread adoption of Large Language Models, conversational recommender systems have become a popular tool for providing recommendations and information. While most recommenders effectively sustain long-term user engagement in real-world applications, they are typically regarded as non-embodied agents, overlooking their social roles in interactions. SOCIALADAPT aims to enable social robots and recommender systems to benefit from each other and achieves the following objectives: for social robots, i) inferring user preferences from multi-modal interactions, and ii) personalized and adaptive responses for long-term use; for recommender systems, iii) integrating conversational recommenders with humanoid embodiments, and iv) evaluating the embodied recommender. To address the first objective, the candidate will use multimodal recommendation methods to infer short-term and long-term user preferences. For the second, she will develop a reinforcement learning method that learns from inferred user preferences to generate adaptive responses. The third objective entails integrating recommendations with virtual humanoid embodiments generated by generative AI. For the fourth, she will evaluate users’ social presence when interacting with the recommender through use studies, in compliance with data privacy. The candidate expects to advance her research career and contribute to the intersection of social robotics and recommender systems, becoming one of the leaders.