  Multisensory polices are known to enhance both state estimation and target
tracking. However, in the space of end-to-end sensorimotor control, this
multi-sensor outlook has received limited attention. Moreover, systematic ways
to make policies robust to partial sensor failure are not well explored. In
this work, we propose a specific customization of Dropout, called
\textit{Sensor Dropout}, to improve multisensory policy robustness and handle
partial failure in the sensor-set. We also introduce an additional auxiliary
loss on the policy network in order to reduce variance in the band of potential
multi- and uni-sensory policies to reduce jerks during policy switching
triggered by an abrupt sensor failure or deactivation/activation. Finally,
through the visualization of gradients, we show that the learned policies are
conditioned on the same latent states representation despite having diverse
observations spaces - a hallmark of true sensor-fusion. Simulation results of
the multisensory policy, as visualized in TORCS racing game, can be seen here:
https://youtu.be/QAK2lcXjNZc.
