Integrating Activity Theory and Distributed Cognition in the Design and Analysis of Social Robots

This paper explores the design and integration of social robots within human environments, applying Activity Theory and Distributed Cognition frameworks to better understand human-robot interaction (HRI). Social robots leverage machine learning algorithms for adaptive decision-making, sensor fusion for perception, and natural language processing (NLP) techniques for communication. This paper evaluates how deep learning models and reinforcement learning-based adaptive behavior enable robots to navigate complex social spaces efficiently. Additionally, AI-driven cognitive architectures and interaction techniques like proxemics, experience prototyping, and user enactments are considered for effective HRI design. This approach provides valuable insights into developing AI-powered social robots that align with social norms and cognitive frameworks in humancentered settings.