Model-Based Robot Imitation with Future Image Similarity

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 from a collection of expert trajectories consisting of unlabeled example videos and actions, and by enabling action selection using future image similarity. In this approach, the robot learns to visually imagine the consequences of taking an action, and obtains the policy by evaluating how similar the predicted future image is to an expert sample. We develop an action-conditioned convolutional autoencoder, and present how we take advantage of future images for zero-online-trial imitation learning. We conduct experiments in simulated and real-life environments using a ground mobility robot with and without obstacles in reaching target objects. We explicitly compare our models to multiple baseline methods requiring only offline samples. The results confirm that our proposed methods perform superior to previous methods, including 1.5 \(\times \) and 2.5 \(\times \) higher success rate in two different tasks than behavioral cloning.