Abstract

Socially Assistive Robots (SARs) have gained significant attention for their ability to interact with people and support them across settings such as healthcare, education, and eldercare. However, effective decision-making in human-robot interaction is a key challenge, particularly in environments with uncertainty. This paper presents a Markov Decision Process (MDP)-based framework to model emotional behavior and drive management in SARS, enabling real-time decision-making under uncertainty. The framework introduces a novel decomposition approach that separates the full MDP framework into emotion management and drive management MDPs, thereby reducing computational complexity as well as the response time. Transition probabilities are modeled using Bayesian networks, and the reward functions are designed to optimize both emotional and motivational engagement. Simulations demonstrate that the divided MDP achieves faster human persuasion with over a 99% reduction in computation time compared to the centralized model. The model also dynamically adapts to variations in human attention levels and environmental distractions. Future work will address the partial observability of emotional States to enhance robustness and real-world applicability. These results establish a foundation for more responsive, autonomous, and socially assistive robots.

About the Speaker

Mr. Jamilu Umar Yahaya is a Ph.D. student in the Control and Instrumentation Engineering Department at King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. He received his M.Sc. degree from the same department in 2025, and his B.Sc. degree in Mechatronics Engineering from Bayero University Kano (BUK), Nigeria, in 2019. His research interests include Human-Robot Interaction and Human Intent Prediction. His research has resulted in several peer-reviewed journal articles and international conferences.