Visual Navigation via Spatio-temporal Adaptive Attention and Meta-Variational Policy Algorithm

Abstract With the widespread deployment of mobile robots in complex task scenarios, enhancing their ability to achieve efficient autonomous navigation in unknown environments has become a major challenge in the field of intelligent robotics. Traditional deep reinforcement learning (DRL) methods often struggle to capture and model long-term spatio-temporal dependencies, resulting in unstable navigation policies and poor generalization in unseen environments. To address these issues, this paper proposes a vision-based navigation framework that integrates a Spatio-temporal Adaptive Convolutional Attention Model (SACAM) and an Asynchronous Meta-Variational Policy Algorithm (AMPA). SACAM enhances the agent’s ability to capture long-term dependencies through self-supervised learning, improving adaptability in complex settings. Meanwhile, AMPA employs an asynchronous update mechanism and a meta-variational approach to handle policy uncertainty, enabling robust decision-making and rapid adaptation. Experimental results in the AI2-THOR simulation environment demonstrate that the proposed method outperforms several advanced baselines in terms of success rate, path efficiency, and generalization capability, showcasing its robust performance and adaptability in challenging navigation tasks.