  A core challenge for an agent learning to interact with the world is to
predict how its actions affect objects in its environment. Many existing
methods for learning the dynamics of physical interactions require labeled
object information. However, to scale real-world interaction learning to a
variety of scenes and objects, acquiring labeled data becomes increasingly
impractical. To learn about physical object motion without labels, we develop
an action-conditioned video prediction model that explicitly models pixel
motion, by predicting a distribution over pixel motion from previous frames.
Because our model explicitly predicts motion, it is partially invariant to
object appearance, enabling it to generalize to previously unseen objects. To
explore video prediction for real-world interactive agents, we also introduce a
dataset of 59,000 robot interactions involving pushing motions, including a
test set with novel objects. In this dataset, accurate prediction of videos
conditioned on the robot's future actions amounts to learning a "visual
imagination" of different futures based on different courses of action. Our
experiments show that our proposed method produces more accurate video
predictions both quantitatively and qualitatively, when compared to prior
methods.
