  Humans can routinely follow a trajectory defined by a list of
images/landmarks. However, traditional robot navigation methods require
accurate mapping of the environment, localization, and planning. Moreover,
these methods are sensitive to subtle changes in the environment. In this
paper, we propose a Deep Visual MPC-policy learning method that can perform
visual navigation while avoiding collisions with unseen objects on the
navigation path. Our model PoliNet takes in as input a visual trajectory and
the image of the robot's current view and outputs velocity commands for a
planning horizon of $N$ steps that optimally balance between trajectory
following and obstacle avoidance. PoliNet is trained using a strong image
predictive model and traversability estimation model in a MPC setup, with
minimal human supervision. Different from prior work, PoliNet can be applied to
new scenes without retraining. We show experimentally that the robot can follow
a visual trajectory when varying start position and in the presence of
previously unseen obstacles. We validated our algorithm with tests both in a
realistic simulation environment and in the real world. We also show that we
can generate visual trajectories in simulation and execute the corresponding
path in the real environment. Our approach outperforms classical approaches as
well as previous learning-based baselines in success rate of goal reaching,
sub-goal coverage rate, and computational load.
