Object Goal Navigation using Goal-Oriented Semantic Exploration

TL;DR

This paper introduces a modular goal-oriented semantic exploration system for object goal navigation that builds semantic maps to improve exploration efficiency, outperforming existing methods in simulation and real-world transfer.

Contribution

The paper presents a novel modular system that leverages semantic mapping and priors for efficient exploration, outperforming end-to-end and map-based baselines in unseen environments.

Findings

Outperforms baseline methods in simulation environments.

Learns semantic priors for object arrangement.

Successfully transfers to real-world robot navigation.

Abstract

This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments. End-to-end learning-based navigation methods struggle at this task as they are ineffective at exploration and long-term planning. We propose a modular system called, `Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently based on the goal object category. Empirical results in visually realistic simulation environments show that the proposed model outperforms a wide range of baselines including end-to-end learning-based methods as well as modular map-based methods and led to the winning entry of the CVPR-2020 Habitat ObjectNav Challenge. Ablation analysis indicates that the proposed model learns semantic priors of the relative arrangement of objects in a scene, and uses them to explore efficiently. Domain-agnostic module design allow us to transfer our model to a mobile robot platform and achieve similar performance for object goal navigation in the real-world.