Learning to Gaze: Bioinspired Attention Adaptation Strategy for Social Robots

Adaptive attention allocation in dynamic social environments remains a fundamental challenge for autonomous robots, requiring the integration of perceptual saliency, social context, and real-time decision-making.We present a bio-inspired reinforcement learning framework for robotic gaze control that incorporates a habituation mechanism to regulate the exploration–exploitation trade-off, mirroring how biological attention systems filter redundant stimuli whilst remaining responsive to novel events. Through a comprehensive ablation study comparing Deep Q-Learning (DQL), Vanilla Q-Learning (VQL), and Multi-Objective Q-Learning (MOL), we uncover a critical insight: habituation significantly enhances DQL performance, improving response efficiency and policy stability, yet causes systematic degradation in MOL due to fundamental incompatibilities between fixed-threshold resets and the extended episodes required for multi-objective optimisation. This differential effect reveals that bio-inspired mechanisms cannot be applied universally across learning architectures but must be carefully matched to algorithmic characteristics. Real-world deployment on the ARI humanoid robot validates the framework’s practical applicability, achieving robust gaze prediction accuracy across diverse interaction scenarios with well-calibrated confidence metrics that reliably distinguish correct from incorrect predictions. Our findings provide evidence-based guidelines for integrating biological principles into cognitive robotics, demonstrating both the promise and the pitfalls of bio-inspired mechanism design.