In this paper, we present a novel framework for robotic attention modulation that enables dynamic regulation between attention-seeking and attention-avoidance behaviours through gaze feedback. For this feedback, we incorporate SGE with gaze fixation counts to create a metric that evaluates not just the quantity but also the quality of visual attention. Our approach implements two complementary modules, a Gaze Avoidance Module (GAM) and a Gaze Garnering Module (GGM), both powered by reinforcement learning (RL) algorithms that respond to real-time human gaze patterns. Thus the system continuously adapts to each individual’s current gaze feedback level, dynamically calibrating the robot’s attention-modulating behaviours to match personal attention thresholds. This personalisation ensures that the robot can effectively engage with users when needed, while remaining unobtrusive when appropriate, all based on real-time analysis of human gaze patterns.
N. H. Wijesinghe, M. Jayasuriya, D. Hinwood, J. B. Grant and D. Herath, “Adaptive Gaze Modulation in Social Robots: A Reinforcement Learning Approach to Attention Regulation,” 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hangzhou, China, 2025, pp. 9217-9222, doi: 10.1109/IROS60139.2025.11247106. keywords: {Measurement;Visualization;Social robots;Reinforcement learning;Regulation;Real-time systems;Trajectory;Indexes;Reliability;Intelligent robots}