  We present an approach to learn an object-centric forward model, and show
that this allows us to plan for sequences of actions to achieve distant desired
goals. We propose to model a scene as a collection of objects, each with an
explicit spatial location and implicit visual feature, and learn to model the
effects of actions using random interaction data. Our model allows capturing
the robot-object and object-object interactions, and leads to more
sample-efficient and accurate predictions. We show that this learned model can
be leveraged to search for action sequences that lead to desired goal
configurations, and that in conjunction with a learned correction module, this
allows for robust closed loop execution. We present experiments both in
simulation and the real world, and show that our approach improves over
alternate implicit or pixel-space forward models. Please see our project page
(https://judyye.github.io/ocmpc/) for result videos.
