Learning Embodied Agents with Policy Gradients to Navigate in Realistic Environments

Indoor navigation is one of the main tasks in robotic systems. Most decisions in this area rely on ideal agent coordinates and a pre-known room map. However, the high accuracy of indoor localization cannot be achieved in realistic scenarios. For example, the GPS has low accuracy in the room; odometry often gives much noise for accurate positioning, etc. In this paper, we conducted a study of the navigation problem in the realistic Habitat simulator. We proposed a method based on the neural network approach and reinforcement learning that takes into account these factors. The most promising recent approaches were DDPPO and ANM for agent control and DF-VO for localization, during the analysis of which a new approach was developed. This method takes into account the non-determinism of the robot’s actions and the noise level of data from its sensors.