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.

利用深度强化学习模型，基于无序的 actor-critic 框架和适当的奖励规范，提出了一个适用于多目标导航的机器人的算法，通过实验验证了这个方法在实践应用中胜过了预设顺序的方法和当下最先进的单个目标导航的方法。