  Embodied AI has been recently gaining attention as it aims to foster the
development of autonomous and intelligent agents. In this paper, we devise a
novel embodied setting in which an agent needs to explore a previously unknown
environment while recounting what it sees during the path. In this context, the
agent needs to navigate the environment driven by an exploration goal, select
proper moments for description, and output natural language descriptions of
relevant objects and scenes. Our model integrates a novel self-supervised
exploration module with penalty, and a fully-attentive captioning model for
explanation. Also, we investigate different policies for selecting proper
moments for explanation, driven by information coming from both the environment
and the navigation. Experiments are conducted on photorealistic environments
from the Matterport3D dataset and investigate the navigation and explanation
capabilities of the agent as well as the role of their interactions.
