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