The Multi-Object Navigation (MultiON) task requires a robot to localize an instance (each) of multiple object classes. It is a fundamental task for an assistive robot in a home or a factory. Existing methods for MultiON have viewed this as a direct extension of Object Navigation (ON), the task of localising an instance of one object class, and are pre-sequenced, i.e., the sequence in which the object classes are to be explored is provided in advance. This is a strong limitation in practical applications characterized by dynamic changes. This paper describes a deep reinforcement learning framework for sequence-agnostic MultiON based on an actor-critic architecture and a suitable reward specification. Our framework leverages past experiences and seeks to reward progress toward individual as well as multiple target object classes. We use photo-realistic scenes from the Gibson benchmark dataset in the AI Habitat 3D simulation environment to experimentally show that our method performs better than a pre-sequenced approach and a state of the art ON method extended to MultiON.
@article{arxiv.2305.06178,
title = {Sequence-Agnostic Multi-Object Navigation},
author = {Nandiraju Gireesh and Ayush Agrawal and Ahana Datta and Snehasis Banerjee and Mohan Sridharan and Brojeshwar Bhowmick and Madhava Krishna},
journal= {arXiv preprint arXiv:2305.06178},
year = {2023}
}