  Imitation learning is a data-driven approach to acquiring skills that relies
on expert demonstrations to learn a policy that maps observations to actions.
When performing demonstrations, experts are not always consistent and might
accomplish the same task in slightly different ways. In this paper, we
demonstrate inherent stochasticity in demonstrations collected for tasks
including line following with a remote-controlled car and manipulation tasks
including reaching, pushing, and picking and placing an object. We model
stochasticity in the data distribution using autoregressive action generation,
generative adversarial nets, and variational prediction and compare the
performance of these approaches. We find that accounting for stochasticity in
the expert data leads to substantial improvement in the success rate of task
completion.
