Adaptive Strategy Weighting with Fault Tolerant Localization for Object Navigation

Yanwei Zheng, Shaopu Feng, Bowen Huang, Changrui Li, Xiao Zhang, Dongxiao Yu

Abstract: End-to-end navigation models commonly incorporate multiple sub-modules, each designed for distinct purposes such as searching, obstacle avoidance, and target localization. However, agents equipped with these modules may still struggle to apply the appropriate strategies at the right locations and stages. For instance, agent might incorrectly rely on the search or localization module for obstacle avoidance, reducing adaptability in dynamic environments. Additionally, existing methods assume the recognition for target object is always correct, neglecting the unavoidable misclassification caused by visually similar objects. To apply appropriate strategy for a given situation, we introduce Adaptive Strategy Feature Fusion (ASFF). It heuristically assigns appropriate weights to different sub-modules based on current observation and memory state, enabling flexible integration with arbitrary sub-module combinations. To improve localization in the presence of misclassification, we propose Fault Tolerant Target Memory Aggregator (FTTMA), a module that uses clustering-based sparse self-attention and target cross-attention to minimize interference from misclassified object, providing accurate target orientation to the agent. Experiments on the AI2THOR and RoboTHOR datasets, including both typical and zero-shot navigation tasks, demonstrate that our model outperforms the state-of-the-art (SOTA) methods in both success rate and navigation efficiency.

External IDs:dblp:conf/icmcs/ZhengFHLZY25