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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 tabletop/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.

```
@misc{cgdf2024,
title={Constrained 6-DoF Grasp Generation on Complex Shapes for Improved Dual-Arm Manipulation}
author={Gaurav Singh and Sanket Kalwar and Md Faizal Karim and Bipasha Sen and Nagamanikandan Govindan and Srinath Sridhar and K Madhava Krishna},
year={2024},
journal={}
}
```