We propose a simple but effective modular approach MOPA (Modular ObjectNav with PointGoal agents) to systematically investigate the inherent modularity of the object navigation task in Embodied AI. MOPA consists of four modules: (a) an object detection module trained to identify objects from RGB images, (b) a map building module to build a semantic map of the observed objects, (c) an exploration module enabling the agent to explore the environment, and (d) a navigation module to move to identified target objects. We show that we can effectively reuse a pretrained PointGoal agent as the navigation model instead of learning to navigate from scratch, thus saving time and compute. We also compare various exploration strategies for MOPA and find that a simple uniform strategy significantly outperforms more advanced exploration methods.
@article{arxiv.2304.03696,
title = {MOPA: Modular Object Navigation with PointGoal Agents},
author = {Sonia Raychaudhuri and Tommaso Campari and Unnat Jain and Manolis Savva and Angel X. Chang},
journal= {arXiv preprint arXiv:2304.03696},
year = {2024}
}