- Our work focuses on the Multi-Object Navigation (MultiON) task, where an agent needs to - navigate to - multiple objects in a given sequence. We systematically investigate the inherent modularity - of this - task by dividing our approach to contain 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 its surroundings, and - finally (d) a - navigation module to move to identified target objects. We focus on the navigation and the - exploration modules in this work. We show that we can effectively leverage a PointGoal - navigation - model in the MultiON task instead of learning to navigate from scratch. Our experiments show - that a - PointGoal agent-based navigation module outperforms analytical path planning on the MultiON - task. We - also compare exploration strategies and surprisingly find that a random exploration strategy - significantly outperforms more advanced exploration methods. We additionally create MultiON - 2.0, a - new large-scale dataset as a test-bed for our approach. + 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.