  In order to anticipate dangerous events, like a collision, an agent needs to
make long-term predictions. However, those are challenging due to uncertainties
in internal and external variables and environment dynamics. A sensorimotor
model is acquired online by the mobile robot using a state-of-the-art method
that learns the optical flow distribution in images, both in space and time.
The learnt model is used to anticipate the optical flow up to a given time
horizon and to predict an imminent collision by using reinforcement learning.
We demonstrate that multi-modal predictions reduce to simpler distributions
once actions are taken into account.
