  The field of robotics has made significant advances towards generalist robot
manipulation policies. However, real-world evaluation of such policies is not
scalable and faces reproducibility challenges, which are likely to worsen as
policies broaden the spectrum of tasks they can perform. We identify control
and visual disparities between real and simulated environments as key
challenges for reliable simulated evaluation and propose approaches for
mitigating these gaps without needing to craft full-fidelity digital twins of
real-world environments. We then employ these approaches to create SIMPLER, a
collection of simulated environments for manipulation policy evaluation on
common real robot setups. Through paired sim-and-real evaluations of
manipulation policies, we demonstrate strong correlation between policy
performance in SIMPLER environments and in the real world. Additionally, we
find that SIMPLER evaluations accurately reflect real-world policy behavior
modes such as sensitivity to various distribution shifts. We open-source all
SIMPLER environments along with our workflow for creating new environments at
https://simpler-env.github.io to facilitate research on general-purpose
manipulation policies and simulated evaluation frameworks.
