Adaptive Robot Behavior Based on Human Comfort Using Reinforcement Learning

This study explores the potential of training robots using reinforcement learning (RL) to adapt their behavior based on human comfort levels during tasks. An experimental environment has been developed and made available to the research community, facilitating the replication of these experiments. The results demonstrate that adjusting a single comfort-related input parameter during training leads to significant variations in the robot's behavior. Detailed discussions of the reward functions and obtained results validate these behavioral adaptations, confirming that robots can dynamically respond to human needs, thereby enhancing human-robot interaction. While the study highlights the effectiveness of this approach, it also raises the question of real-time comfort measurement, suggesting various systems for future exploration. These findings contribute to the development of more intuitive and emotionally responsive robots, offering new possibilities for future research in advancing human-robot interaction.