  We present Simitate --- a hybrid benchmarking suite targeting the evaluation
of approaches for imitation learning. A dataset containing 1938 sequences where
humans perform daily activities in a realistic environment is presented. The
dataset is strongly coupled with an integration into a simulator. RGB and depth
streams with a resolution of 960$\mathbb{\times}$540 at 30Hz and accurate
ground truth poses for the demonstrator's hand, as well as the object in 6 DOF
at 120Hz are provided. Along with our dataset we provide the 3D model of the
used environment, labeled object images and pre-trained models. A benchmarking
suite that aims at fostering comparability and reproducibility supports the
development of imitation learning approaches. Further, we propose and integrate
evaluation metrics on assessing the quality of effect and trajectory of the
imitation performed in simulation. Simitate is available on our project
website: \url{https://agas.uni-koblenz.de/data/simitate/}.
