This paper focuses on the sim-to-real issue of RGB-D grasp detection and
formulates it as a domain adaptation problem. In this case, we present a
global-to-local method to address hybrid domain gaps in RGB and depth data and
insufficient multi-modal feature alignment. First, a self-supervised rotation
pre-training strategy is adopted to deliver robust initialization for RGB and
depth networks. We then propose a global-to-local alignment pipeline with
individual global domain classifiers for scene features of RGB and depth images
as well as a local one specifically working for grasp features in the two
modalities. In particular, we propose a grasp prototype adaptation module,
which aims to facilitate fine-grained local feature alignment by dynamically
updating and matching the grasp prototypes from the simulation and real-world
scenarios throughout the training process. Due to such designs, the proposed
method substantially reduces the domain shift and thus leads to consistent
performance improvements. Extensive experiments are conducted on the
GraspNet-Planar benchmark and physical environment, and superior results are
achieved which demonstrate the effectiveness of our method.

该论文通过领域适应问题将 RGB-D 抓握检测的问题转变为 sim-to-real 问题，并提出了一种全局到局部的方法来解决 RGB 和深度数据之间的差距以及多模态特征对齐的不足。在大量实验中，该方法显著减小了领域转移，并获得了令人满意的结果，证明了其有效性。