  In this paper, we introduce DA$^2$, the first large-scale dual-arm
dexterity-aware dataset for the generation of optimal bimanual grasping pairs
for arbitrary large objects. The dataset contains about 9M pairs of
parallel-jaw grasps, generated from more than 6000 objects and each labeled
with various grasp dexterity measures. In addition, we propose an end-to-end
dual-arm grasp evaluation model trained on the rendered scenes from this
dataset. We utilize the evaluation model as our baseline to show the value of
this novel and nontrivial dataset by both online analysis and real robot
experiments. All data and related code will be open-sourced at
https://sites.google.com/view/da2dataset.
