  We present an approach for physical imitation from human videos for robot
manipulation tasks. The key idea of our method lies in explicitly exploiting
the kinematics and motion information embedded in the video to learn structured
representations that endow the robot with the ability to imagine how to perform
manipulation tasks in its own context. To achieve this, we design a perception
module that learns to translate human videos to the robot domain followed by
unsupervised keypoint detection. The resulting keypoint-based representations
provide semantically meaningful information that can be directly used for
reward computing and policy learning. We evaluate the effectiveness of our
approach on five robot manipulation tasks, including reaching, pushing,
sliding, coffee making, and drawer closing. Detailed experimental evaluations
demonstrate that our method performs favorably against previous approaches.
