  There has been a significant recent progress in the field of Embodied AI with
researchers developing models and algorithms enabling embodied agents to
navigate and interact within completely unseen environments. In this paper, we
propose a new dataset and baseline models for the task of Rearrangement. We
particularly focus on the task of Room Rearrangement: an agent begins by
exploring a room and recording objects' initial configurations. We then remove
the agent and change the poses and states (e.g., open/closed) of some objects
in the room. The agent must restore the initial configurations of all objects
in the room. Our dataset, named RoomR, includes 6,000 distinct rearrangement
settings involving 72 different object types in 120 scenes. Our experiments
show that solving this challenging interactive task that involves navigation
and object interaction is beyond the capabilities of the current
state-of-the-art techniques for embodied tasks and we are still very far from
achieving perfect performance on these types of tasks. The code and the dataset
are available at: https://ai2thor.allenai.org/rearrangement
