Efficiently generating grasp poses tailored to specific regions of an object
is vital for various robotic manipulation tasks, especially in a dual-arm
setup. This scenario presents a significant challenge due to the complex
geometries involved, requiring a deep understanding of the local geometry to
generate grasps efficiently on the specified constrained regions. Existing
methods only explore settings involving table-top/small objects and require
augmented datasets to train, limiting their performance on complex objects. We
propose CGDF: Constrained Grasp Diffusion Fields, a diffusion-based grasp
generative model that generalizes to objects with arbitrary geometries, as well
as generates dense grasps on the target regions. CGDF uses a part-guided
diffusion approach that enables it to get high sample efficiency in constrained
grasping without explicitly training on massive constraint-augmented datasets.
We provide qualitative and quantitative comparisons using analytical metrics
and in simulation, in both unconstrained and constrained settings to show that
our method can generalize to generate stable grasps on complex objects,
especially useful for dual-arm manipulation settings, while existing methods
struggle to do so.

提出了一种基于扩散的抓取生成模型 CGDF（Constrained Grasp Diffusion Fields），它可以适应任意物体的复杂几何形状，并在目标区域上生成密集的抓取。通过使用部分引导的扩散方法，实现了高效率的约束抓取，无需通过大量约束增强数据集进行训练。通过定量和定性的比较分析结果以及在模拟中的表现，验证了该方法可以生成稳定的抓取姿势，尤其适用于双臂操作设置，而现有方法则存在困难。