  Learning long-term dynamics models is the key to understanding physical
common sense. Most existing approaches on learning dynamics from visual input
sidestep long-term predictions by resorting to rapid re-planning with
short-term models. This not only requires such models to be super accurate but
also limits them only to tasks where an agent can continuously obtain feedback
and take action at each step until completion. In this paper, we aim to
leverage the ideas from success stories in visual recognition tasks to build
object representations that can capture inter-object and object-environment
interactions over a long-range. To this end, we propose Region Proposal
Interaction Networks (RPIN), which reason about each object's trajectory in a
latent region-proposal feature space. Thanks to the simple yet effective object
representation, our approach outperforms prior methods by a significant margin
both in terms of prediction quality and their ability to plan for downstream
tasks, and also generalize well to novel environments. Code, pre-trained
models, and more visualization results are available at https://haozhi.io/RPIN.
