A Neural-Symbolic Approach for Object Navigation


Object navigation refers to the task of discovering and locating objects in an unknown environment. End-to-end deep learning methods struggle at this task due to sparse rewards. In this work, we propose a simple neural-symbolic approach for object navigation in the AI2-THOR environment. Our method takes raw RGB images as input and uses a spatial memory graph as memory to store object and location information. The architecture consists of both a convolutional neural network for object detection and a spatial graph to represent the environment. By having a discrete graph representation of the environment, the agent can directly use search or planning algorithms as high-level reasoning engines. Model performance is evaluated on both task completion rate and steps required to reach target objects. Empirical results demonstrate that our approach can achieve performance close to the optimal. Our work builds a foundation for a neural-symbolic approach that can reason via unstructured visual cues.

CVPR Embodied AI Workshop 2021