Task-relevant grasping is critical for industrial assembly, where downstream
manipulation tasks constrain the set of valid grasps. Learning how to perform
this task, however, is challenging, since task-relevant grasp labels are hard
to define and annotate. There is also yet no consensus on proper
representations for modeling or off-the-shelf tools for performing
task-relevant grasps. This work proposes a framework to learn task-relevant
grasping for industrial objects without the need of time-consuming real-world
data collection or manual annotation. To achieve this, the entire framework is
trained solely in simulation, including supervised training with synthetic
label generation and self-supervised, hand-object interaction. In the context
of this framework, this paper proposes a novel, object-centric canonical
representation at the category level, which allows establishing dense
correspondence across object instances and transferring task-relevant grasps to
novel instances. Extensive experiments on task-relevant grasping of
densely-cluttered industrial objects are conducted in both simulation and
real-world setups, demonstrating the effectiveness of the proposed framework.
Code and data are available at this https URL

通过模拟学习，本文提出了一种用于学习工业物体的任务相关抓取的框架，同时提出了一种新颖的分类层面的物体中心规范表示，可以在物体实例之间建立密集通信，并将任务相关抓取传递到新实例中。