  Deep reinforcement learning is becoming increasingly popular for robot
control algorithms, with the aim for a robot to self-learn useful feature
representations from unstructured sensory input leading to the optimal
actuation policy. In addition to sensors mounted on the robot, sensors might
also be deployed in the environment, although these might need to be accessed
via an unreliable wireless connection. In this paper, we demonstrate deep
neural network architectures that are able to fuse information coming from
multiple sensors and are robust to sensor failures at runtime. We evaluate our
method on a search and pick task for a robot both in simulation and the real
world.
