Commonsense-Guided Object Graph Reasoning with Policy Regularization for Object Goal Navigation

Abstract

Object goal navigation aims to guide an agent to find a specific target object in an unseen environment using only first-person visual observations. It requires the agent to enhance scene understanding and train a robust navigation policy. To address this, we proposed two complementary techniques, commonsense-guided object graph reasoning (COGR) and policy regularization (PR). Specifically, COGR improves the agent's scene understanding by integrating object relationships, including category proximity and spatial correlation. It extracts co-occurrence embeddings of the target object from a large language model (LLM) as commonsense knowledge to guide object graph reasoning, enabling the agent to reason beyond visual co-occurrence observed in training environments. PR is a knowledge distillation-inspired regularization mechanism, where a commonsense-free model is used to regularize the navigation policy of the commonsense-guided model. We propose PR to mitigate potential performance degradation caused by knowledge bias from the LLM, enabling the training of a more robust navigation policy. Experiments in the AI2-Thor and RoboThor environments demonstrate the effectiveness and efficiency of our proposed method, and real-world deployment further validates its transferability.