Short Bio

Topic

Developing socially aware robots requires the ability to interpret context, follow social norms, recognize cues, and process feedback from both the environment and human interactions. Effective communication through verbal and non-verbal behaviors is also essential. While some research addresses social norms, few robots exhibit comprehensive social awareness. This thesis explores how robots can learn and adapt to social norms—collective behaviors shaped by approval and disapproval—through reinforcement learning and game theory. It will examine both widely accepted conventional norms and essential norms that structure interactions. The key objectives are to:(i) Define a model of social norms;(ii) Develop a method based on reinforcement learning and game theory to enable the robot to learn and adapt its behaviour based on the context and social norms, and(ii) Evaluate the model’s performance in real-time situations, thereby enhancing the competence and adaptability of robotic systems with a focus on adhering to social norms. 

Host Institution: École nationale supérieure de techniques avancées Paris – Paris, France

PhD Enrollment: École nationale supérieure de techniques avancées Paris

PI: Prof. Adriana Tapus