Context-Aware Graph Inference and Generative Adversarial Imitation Learning for Object-Goal Navigation in Unfamiliar Environment

Abstract

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 proposed two complementary techniques, context-aware graph inference (CGI) and generative adversarial imitation learning (GAIL). CGI improves visual representation learning by integrating object relationships, including category proximity and spatial correlation. It uses the translation on hyperplane (TransH) method to infer context-aware object relationships under the guidance of various contexts over navigation episodes, including image, action, and memory. Both CGI and GAIL aim to improve robust navigation policy, enabling the agent to escape from deadlock states, such as looping or getting stuck. GAIL is an imitation learning (IL) technique that enables the agent to learn from expert demonstrations. Specifically, we propose GAIL to address the non-discriminative reward problem that exists in object-goal navigation. GAIL designs a dynamic reward function and combines it with environment rewards, thus providing guidance for effective navigation policy. Experiments in the AI2-Thor and RoboThor environments demonstrate that our method significantly improves the effectiveness and efficiency of navigation in unfamiliar environments.