Interactive Semantic Map Representation for Skill-Based Visual Object Navigation

Visual object navigation is one of the key tasks in mobile robotics. One of the most important components of this task is the accurate semantic representation of the scene, which is needed to determine and reach a goal object. This paper introduces a new representation of a scene semantic map formed during the embodied agent interaction with the indoor environment. It is based on a neural network method that adjusts the weights of the segmentation model with backpropagation of the predicted fusion loss values during inference on a regular (backward) or delayed (forward) image sequence. We implement this representation into a full-fledged navigation approach called SkillTron. The method can select robot skills from end-to-end policies based on reinforcement learning and classic map-based planning methods. The proposed approach makes it possible to form both intermediate goals for robot exploration and the final goal for object navigation. We conduct intensive experiments with the proposed approach in the Habitat environment, demonstrating its significant superiority over state-of-the-art approaches in terms of navigation quality metrics. The developed code and custom datasets are publicly available at github.com/AIRI-Institute/ skill-fusion.