  We describe a framework for research and evaluation in Embodied AI. Our
proposal is based on a canonical task: Rearrangement. A standard task can focus
the development of new techniques and serve as a source of trained models that
can be transferred to other settings. In the rearrangement task, the goal is to
bring a given physical environment into a specified state. The goal state can
be specified by object poses, by images, by a description in language, or by
letting the agent experience the environment in the goal state. We characterize
rearrangement scenarios along different axes and describe metrics for
benchmarking rearrangement performance. To facilitate research and exploration,
we present experimental testbeds of rearrangement scenarios in four different
simulation environments. We anticipate that other datasets will be released and
new simulation platforms will be built to support training of rearrangement
agents and their deployment on physical systems.
