  Event-based cameras are dynamic vision sensors that 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 asynchronously streamed event data poses a challenge for machine
learning techniques that are more suited for frame-based data. In this paper,
we present an event variational autoencoder and show that it is feasible to
learn compact representations directly from asynchronous spatiotemporal event
data. Furthermore, we show that such pretrained representations can be used for
event-based reinforcement learning instead of end-to-end reward driven
perception. We validate this framework of learning event-based visuomotor
policies by applying it to an obstacle avoidance scenario in simulation.
Compared to techniques that treat event data as images, we show that
representations learnt from event streams result in faster policy training,
adapt to different control capacities, and demonstrate a higher degree of
robustness.
