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.

翻译：我们提出CAPGrasp，一种$\mathbb{R}^3\times \text{SO(2)-等变}$的六自由度连续路径约束生成式抓取采样器。该工作包含一种用于训练CAPGrasp的新型学习策略，无需构建大规模条件标注数据集；同时提出一种约束抓取精化技术，可在遵循抓取路径方向约束的前提下优化抓取姿态。实验结果表明，与无约束抓取采样器相比，CAPGrasp的样本效率提升三倍以上，抓取成功率最高提升38%。相较于受约束但非连续的抓取采样器，CAPGrasp的抓取成功率仍提高4-10%。总体而言，当抓取必须源自特定方向时（如在受限空间内进行抓取），CAPGrasp是一种样本高效的解决方案。