  We propose a framework for robust and efficient training of Dense Object Nets
(DON) with a focus on multi-object robot manipulation scenarios. DON is a
popular approach to obtain dense, view-invariant object descriptors, which can
be used for a multitude of downstream tasks in robot manipulation, such as,
pose estimation, state representation for control, etc.. However, the original
work focused training on singulated objects, with limited results on
instance-specific, multi-object applications. Additionally, a complex data
collection pipeline, including 3D reconstruction and mask annotation of each
object, is required for training. In this paper, we further improve the
efficacy of DON with a simplified data collection and training regime, that
consistently yields higher precision and enables robust tracking of keypoints
with less data requirements. In particular, we focus on training with
multi-object data instead of singulated objects, combined with a well-chosen
augmentation scheme. We additionally propose an alternative loss formulation to
the original pixelwise formulation that offers better results and is less
sensitive to hyperparameters. Finally, we demonstrate the robustness and
accuracy of our proposed framework on a real-world robotic grasping task.
