Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation
This addresses the challenge of enabling robots to imitate behaviors from diverse video sources, such as human tool use, which is incremental by extending imitation learning to more flexible observation settings.
The paper tackles the problem of imitation learning from raw video observations without requiring the same environment configuration as the demonstrator, proposing a method based on video prediction with context translation and deep reinforcement learning. The result is effective learning of real-world robotic tasks like sweeping and ladling almonds from human videos, with demonstrations in both simulation and household chore scenarios.
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, object positions and types, and other factors. We term this kind of imitation learning "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 in the same environment configuration, 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 the effectiveness of our approach in learning a wide range of real-world robotic tasks modeled after common household chores from videos of a human demonstrator, including sweeping, ladling almonds, pushing objects as well as a number of tasks in simulation.