Learning to Gaze: Bioinspired Attention Adaptation Strategy for Social Robots

Abstract

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 bioinspired 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 while remaining responsive to novel events. Through a comprehensive ablation study comparing deep Q-learning (DQL), Vanilla Q-learning (VQL), and multiibjective 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 multiobjective optimisation. This differential effect reveals that bioinspired 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 bioinspired mechanism design.