Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation
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Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. However, standard imitation learning methods assume that the agent receives examples of observation-action tuples that could be provided, for instance, to a supervised learning algorithm. This stands in contrast to how humans and animals imitate: we observe another person performing some behavior and then figure out which actions will realize that behavior, compensating for changes in viewpoint, surroundings, and embodiment. We term this kind of imitation learning as imitation-from-observation and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning. This lifts the assumption in imitation learning that the demonstration should consist of observations and actions in the same environment, and enables a variety of interesting applications, including learning robotic skills that involve tool use simply by observing videos of human tool use. Our experimental results show that our approach can perform imitation-from-observation for a variety of real-world robotic tasks modeled on common household chores, acquiring skills such as sweeping from videos of a human demonstrator. Videos can be found at https://sites.google.com/site/imitationfromobservation
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