Spatial–Semantic Attention Network With Adaptive Similarity Perception and Memory for Efficient Zero-Shot Object-Goal Visual Navigation

This article investigates efficient zero-shot object-goal visual navigation, where an agent localizes unseen targets in novel environments using only visual observations and target representations. Existing methods suffer from an inability to establish a unified visual representation, leading to complex and inefficient spatial–semantic joint matching. Moreover, current visual navigation frameworks, typically divided into Visual Perception and Policy Learning stages, fail to effectively leverage historical visual features, resulting in a weak connection between perception and decision-making. To overcome these limitations, we propose a novel multistage visual navigation paradigm, the spatial–semantic attention network with adaptive similarity perception and memory (S2AN-ASPM), which integrates three core components: 1) a similarity-aware module (SAM) that learns adaptive spatial–semantic embeddings through bilinear similarity metrics and dynamic attention; 2) a target-oriented memory-augmented (TOMA) branch that explicitly encodes historical trajectories and target positions via multiscale aggregation; and 3) a reinforcement learning (RL) policy optimized with a reward function balancing distance, view angle, and target relevance. To evaluate the navigation performance of S2AN-ASPM, we conducted extensive experiments in the AI2-THOR and Habitat HM3D embodied AI environments. Our method outperforms the previous state-of-the-art on the zero-shot ObjectNav task. The simulation results demonstrate the strong generalizability of S2AN-ASPM when dealing with unseen scenes and target objects. The code is available at https://github.com/zihaodong/S2AN-ASPM