  We present a visual imitation learning framework that enables learning of
robot action policies solely based on expert samples without any robot trials.
Robot exploration and on-policy trials in a real-world environment could often
be expensive/dangerous. We present a new approach to address this problem by
learning a future scene prediction model solely on a collection of expert
trajectories consisting of unlabeled example videos and actions, and by
enabling generalized action cloning using future image similarity. The robot
learns to visually predict the consequences of taking an action, and obtains
the policy by evaluating how similar the predicted future image is to an expert
image. We develop a stochastic action-conditioned convolutional autoencoder,
and present how we take advantage of future images for robot learning. We
conduct experiments in simulated and real-life environments using a ground
mobility robot with and without obstacles, and compare our models to multiple
baseline methods.
