We propose CAPGrasp, an R3×SO(2)-equivariant 6-DoF
continuous approach-constrained generative grasp sampler. It includes a novel
learning strategy for training CAPGrasp that eliminates the need to curate
massive conditionally labeled datasets and a constrained grasp refinement
technique that improves grasp poses while respecting the grasp approach
directional constraints. The experimental results demonstrate that CAPGrasp is
more than three times as sample efficient as unconstrained grasp samplers while
achieving up to 38% grasp success rate improvement. CAPGrasp also achieves
4-10% higher grasp success rates than constrained but noncontinuous grasp
samplers. Overall, CAPGrasp is a sample-efficient solution when grasps must
originate from specific directions, such as grasping in confined spaces.Comment: This work has been submitted to the IEEE for possible publication.
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