基于领域自适应的未知目标分割
Unknown Object Segmentation through Domain Adaptation
摘要：
在杂乱场景中分割未知物体的能力对机器人抓取有着深远的影响。深度学习的兴起极大地改变了机器人抓取的管道，从基于模型的方法到数据驱动的流，这通常需要从仿真或真实世界的例子中收集大规模的抓取数据。在本文中，我们提出了一个模拟到真实的框架，将在模拟中学习的对象分割模型转移到现实世界中。首先，在仿真中收集数据样本，包括RGB，6D姿态和点云。其次，我们还提出了一种基于GAN的领域自适应未知目标分割方法，该方法由图像平移模块和图像分割模块组成。图像平移模块用于缩小真实感差距，分割模块负责分割模板的生成。我们使用上述方法在一个bin-picking场景中对未知对象进行分割实验。实验结果表明，仿真学习的分割模型可以用于实际数据的分割。
英文摘要：
The ability to segment unknown objects in cluttered scenes has a profound impact on robot grasping. The rise of deep learning has greatly transformed the pipeline of robotic grasping from model-based approach to data-driven stream, which generally requires a large scale of grasping data either collected in simulation or from real-world examples. In this paper, we proposed a sim-to-real framework to transfer the object segmentation model learned in simulation to the real-world. First, data samples are collected in simulation, including RGB, 6D pose, and point cloud. Second, we also present a GAN-based unknown object segmentation method through domain adaptation, which consists of an image translation module and an image segmentation module. The image translation module is used to shorten the reality gap and the segmentation module is responsible for the segmentation mask generation. We used the above method to perform segmentation experiments on unknown objects in a bin-picking scenario. Finally, the experimental result shows that the segmentation model learned in simulation can be used for real-world data segmentation.
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