Action-Insensitive Embodied Visual Navigation

Abstract

Embodied visual navigation is an important task that the agent learns to navigate to a specific target object based on egocentric visual observations, by performing specific actions in the environment. However, there exists a problem of mismatch between the training and testing action spaces through learning methods, and methods used to solve this problem have been scarcely developed. In this paper, we propose a novel problem of the action-insensitive embodied visual navigation task with different action spaces of the agent between the training and testing process. A robust adversary learning framework is built to learn a general and robust policy that can adapt properly to different action spaces. The proposed model in the first-stage adversary training learns a robust feature representation of the agent's states and transfers the trained strategy to new action spaces with fewer training samples in the second-stage adaptation training. Experiments on 3D indoor scenes validate the effectiveness of the proposed approach.