  Event-based cameras are dynamic vision sensors that can provide asynchronous
measurements of changes in per-pixel brightness at a microsecond level. This
makes them significantly faster than conventional frame-based cameras, and an
appealing choice for high-speed navigation. While an interesting sensor
modality, this asynchronous data poses a challenge for common machine learning
techniques. In this paper, we present an event variational autoencoder for
unsupervised representation learning from asynchronous event camera data. We
show that it is feasible to learn compact representations from spatiotemporal
event data to encode the context. Furthermore, we show that such pretrained
representations can be beneficial for navigation, allowing for usage in
reinforcement learning instead of end-to-end reward driven perception. We
validate this framework of learning visuomotor policies by applying it to an
obstacle avoidance scenario in simulation. We show that representations learnt
from event data enable training fast control policies that can adapt to
different control capacities, and demonstrate a higher degree of robustness
than end-to-end learning from event images.
