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

Model-based Behavioral Cloning with Future Image Similarity Learning