Constrained 6-DoF Grasp Generation on Complex Shapes for Improved
  Dual-Arm Manipulation
Gaurav Singh Sanket Kalwar Md Faizal Karim Bipasha Sen Nagamanikandan Govindan Srinath Sridhar K Madhava Krishna
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.