  In order to autonomously learn wide repertoires of complex skills, robots
must be able to learn from their own autonomously collected data, without human
supervision. One learning signal that is always available for autonomously
collected data is prediction: if a robot can learn to predict the future, it
can use this predictive model to take actions to produce desired outcomes, such
as moving an object to a particular location. However, in complex open-world
scenarios, designing a representation for prediction is difficult. In this
work, we instead aim to enable self-supervised robotic learning through direct
video prediction: instead of attempting to design a good representation, we
directly predict what the robot will see next, and then use this model to
achieve desired goals. A key challenge in video prediction for robotic
manipulation is handling complex spatial arrangements such as occlusions. To
that end, we introduce a video prediction model that can keep track of objects
through occlusion by incorporating temporal skip-connections. Together with a
novel planning criterion and action space formulation, we demonstrate that this
model substantially outperforms prior work on video prediction-based control.
Our results show manipulation of objects not seen during training, handling
multiple objects, and pushing objects around obstructions. These results
represent a significant advance in the range and complexity of skills that can
be performed entirely with self-supervised robotic learning.
