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In this paper, we present a novel approach to incrementally learn an AbstractModel of an unknown environment, and show how an agent can reuse the learnedmodel for tackling the Object Goal Navigation task. The Abstract Model is afinite state machine in which each state is an abstraction of a state of theenvironment, as perceived by the agent in a certain position and orientation.The perceptions are high-dimensional sensory data (e.g., RGB-D images), and theabstraction is reached by exploiting image segmentation and the Taskonomy modelbank. The learning of the Abstract Model is accomplished by executing actions,observing the reached state, and updating the Abstract Model with the acquiredinformation. The learned models are memorized by the agent, and they are reusedwhenever it recognizes to be in an environment that corresponds to the storedmodel. We investigate the effectiveness of the proposed approach for the ObjectGoal Navigation task, relying on public benchmarks. Our results show that thereuse of learned Abstract Models can boost performance on Object GoalNavigation.

Online Learning of Reusable Abstract Models for Object Goal Navigation