  Autonomous navigation is an essential capability of smart mobility for mobile
robots. Traditional methods must have the environment map to plan a
collision-free path in workspace. Deep reinforcement learning (DRL) is a
promising technique to realize the autonomous navigation task without a map,
with which deep neural network can fit the mapping from observation to
reasonable action through explorations. It should not only memorize the trained
target, but more importantly, the planner can reason out the unseen goal. We
proposed a new motion planner based on deep reinforcement learning that can
arrive at new targets that have not been trained before in the indoor
environment with RGB image and odometry only. The model has a structure of
stacked Long Short-Term memory (LSTM). Finally, experiments were implemented in
both simulated and real environments. The source code is available:
https://github.com/marooncn/navbot.
