  We propose DeepExplorer, a simple and lightweight metric-free exploration
method for topological mapping of unknown environments. It performs task and
motion planning (TAMP) entirely in image feature space. The task planner is a
recurrent network using the latest image observation sequence to hallucinate a
feature as the next-best exploration goal. The motion planner then utilizes the
current and the hallucinated features to generate an action taking the agent
towards that goal. The two planners are jointly trained via deeply-supervised
imitation learning from expert demonstrations. During exploration, we
iteratively call the two planners to predict the next action, and the
topological map is built by constantly appending the latest image observation
and action to the map and using visual place recognition (VPR) for loop
closing. The resulting topological map efficiently represents an environment's
connectivity and traversability, so it can be used for tasks such as visual
navigation. We show DeepExplorer's exploration efficiency and strong sim2sim
generalization capability on large-scale simulation datasets like Gibson and
MP3D. Its effectiveness is further validated via the image-goal navigation
performance on the resulting topological map. We further show its strong
zero-shot sim2real generalization capability in real-world experiments. The
source code is available at \url{https://ai4ce.github.io/DeepExplorer/}.
