  Reinforcement learning can enable complex, adaptive behavior to be learned
automatically for autonomous robotic platforms. However, practical deployment
of reinforcement learning methods must contend with the fact that the training
process itself can be unsafe for the robot. In this paper, we consider the
specific case of a mobile robot learning to navigate an a priori unknown
environment while avoiding collisions. In order to learn collision avoidance,
the robot must experience collisions at training time. However, high-speed
collisions, even at training time, could damage the robot. A successful
learning method must therefore proceed cautiously, experiencing only low-speed
collisions until it gains confidence. To this end, we present an
uncertainty-aware model-based learning algorithm that estimates the probability
of collision together with a statistical estimate of uncertainty. By
formulating an uncertainty-dependent cost function, we show that the algorithm
naturally chooses to proceed cautiously in unfamiliar environments, and
increases the velocity of the robot in settings where it has high confidence.
Our predictive model is based on bootstrapped neural networks using dropout,
allowing it to process raw sensory inputs from high-bandwidth sensors such as
cameras. Our experimental evaluation demonstrates that our method effectively
minimizes dangerous collisions at training time in an obstacle avoidance task
for a simulated and real-world quadrotor, and a real-world RC car. Videos of
the experiments can be found at https://sites.google.com/site/probcoll.
