  The EmbodiedQA is a task of training an embodied agent by intelligently
navigating in a simulated environment and gathering visual information to
answer questions. Existing approaches fail to explicitly model the mental
imagery function of the agent, while the mental imagery is crucial to embodied
cognition, and has a close relation to many high-level meta-skills such as
generalization and interpretation. In this paper, we propose a novel Mental
Imagery eNhanceD (MIND) module for the embodied agent, as well as a relevant
deep reinforcement framework for training. The MIND module can not only model
the dynamics of the environment (e.g. 'what might happen if the agent passes
through a door') but also help the agent to create a better understanding of
the environment (e.g. 'The refrigerator is usually in the kitchen'). Such
knowledge makes the agent a faster and better learner in locating a feasible
policy with only a few trails. Furthermore, the MIND module can generate mental
images that are treated as short-term subgoals by our proposed deep
reinforcement framework. These mental images facilitate policy learning since
short-term subgoals are easy to achieve and reusable. This yields better
planning efficiency than other algorithms that learn a policy directly from
primitive actions. Finally, the mental images visualize the agent's intentions
in a way that human can understand, and this endows our agent's actions with
more interpretability. The experimental results and further analysis prove that
the agent with the MIND module is superior to its counterparts not only in EQA
performance but in many other aspects such as route planning, behavioral
interpretation, and the ability to generalize from a few examples.
