GoNet: An Approach-Constrained Generative Grasp Sampling Network

Constraining the approach direction of grasps is important when picking objects in confined spaces, such as when emptying a shelf. Yet, such capabilities are not available in state-of-the-art data-driven grasp sampling methods that sample grasps all around the object. In this work, we address the specific problem of training approach-constrained data-driven grasp samplers and how to generate good grasping directions automatically. Our solution is GoNet: a generative grasp sampler that can constrain the grasp approach direction to lie close to a specified direction. This is achieved by discretizing SO(3) into bins and training GoNet to generate grasps from those bins. At run-time, the bin aligning with the second largest principal component of the observed point cloud is selected. GoNet is benchmarked against GraspNet, a state-of-the-art unconstrained grasp sampler, in an unconfined grasping experiment in simulation and on an unconfined and confined grasping experiment in the real world. The results demonstrate that GoNet achieves higher success-over-coverage in simulation and a 12%-18% higher success rate in real-world table-picking and shelf-picking tasks than the baseline.Comment: IROS 2023 submissio

CAPGrasp: An R3×SO(2)-equivariant\mathbb{R}^3\times \text{SO(2)-equivariant} Continuous Approach-Constrained Generative Grasp Sampler

We propose CAPGrasp, an $\mathbb{R}^3\times \text{SO(2)-equivariant}$ 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. Copyright may be transferred without notice, after which this version may no longer be accessibl