  Visual recognition ecosystems (e.g. ImageNet, Pascal, COCO) have undeniably
played a prevailing role in the evolution of modern computer vision. We argue
that interactive and embodied visual AI has reached a stage of development
similar to visual recognition prior to the advent of these ecosystems.
Recently, various synthetic environments have been introduced to facilitate
research in embodied AI. Notwithstanding this progress, the crucial question of
how well models trained in simulation generalize to reality has remained
largely unanswered. The creation of a comparable ecosystem for
simulation-to-real embodied AI presents many challenges: (1) the inherently
interactive nature of the problem, (2) the need for tight alignments between
real and simulated worlds, (3) the difficulty of replicating physical
conditions for repeatable experiments, (4) and the associated cost. In this
paper, we introduce RoboTHOR to democratize research in interactive and
embodied visual AI. RoboTHOR offers a framework of simulated environments
paired with physical counterparts to systematically explore and overcome the
challenges of simulation-to-real transfer, and a platform where researchers
across the globe can remotely test their embodied models in the physical world.
As a first benchmark, our experiments show there exists a significant gap
between the performance of models trained in simulation when they are tested in
both simulations and their carefully constructed physical analogs. We hope that
RoboTHOR will spur the next stage of evolution in embodied computer vision.
RoboTHOR can be accessed at the following link:
https://ai2thor.allenai.org/robothor
