Dual object graph and bisimulation metric for object-goal navigation in unfamiliar environment

Object-goal navigation aims to guide an agent to find a specific target object in an unfamiliar environment based on first-person visual observations. It requires the agent to learn informative visual representations and robust navigation policy. To promote these two components, we propose two complementary techniques, dual object graph (DOG) and bisimulation metric (BM). DOG integrates current and historical object relationships, including category proximity and spatial correlation. It constructs a current object graph (COG) to model real-time object relationships and maintains a historical object graph (HOG) to preserve long-term object relationships. DOG improves visual representation learning. Both DOG and BM aim to improve robust navigation policy, enabling the agent to escape from deadlock states, such as looping or getting stuck. Specifically, BM is a self-supervised reinforcement learning (RL) technique that groups behaviorally similar observations in representation space. In the process, BM learns robust latent representations that capture only the task-relevant information from observations against distractions such as variations in background or viewpoint, thus providing guidance for effective navigation policy. Experiments in the AI2-Thor and RoboThor environment demonstrate that our method significantly improves the effectiveness and efficiency of navigation in unfamiliar environments, and real-world deployment further validates its transferability.