Robotic grasping is a fundamental ability for a robot to interact with the
environment. Current methods focus on how to obtain a stable and reliable
grasping pose in object wise, while little work has been studied on part
(shape)-wise grasping which is related to fine-grained grasping and robotic
affordance. Parts can be seen as atomic elements to compose an object, which
contains rich semantic knowledge and a strong correlation with affordance.
However, lacking a large part-wise 3D robotic dataset limits the development of
part representation learning and downstream application. In this paper, we
propose a new large Language-guided SHape grAsPing datasEt (named Lang-SHAPE)
to learn 3D part-wise affordance and grasping ability. We design a novel
two-stage fine-grained robotic grasping network (named PIONEER), including a
novel 3D part language grounding model, and a part-aware grasp pose detection
model. To evaluate the effectiveness, we perform multi-level difficulty part
language grounding grasping experiments and deploy our proposed model on a real
robot. Results show our method achieves satisfactory performance and efficiency
in reference identification, affordance inference, and 3D part-aware grasping.
Our dataset and code are available on our project website
this https URL

本文提出了一种新的大型语言引导的形状抓取数据集（名为 Lang-SHAPE），用于学习 3D 部件的 affordance 和抓握能力，还设计了一种新颖的两阶段细粒度机器人抓握网络（名为 PIONEER）进行模型测试。结果表明，我们的方法在参考标识、affordance 推理和 3D 部分感知抓握方面实现了令人满意的性能和效率。