Dexterous grasping is fundamental to robotics, yet data-driven grasp prediction heavily relies on large, diverse datasets that are costly to generate and typically limited to a narrow set of gripper morphologies. Analytical grasp synthesis can be used to scale data collection, but necessary simplifying assumptions often yield physically infeasible grasps that need to be filtered in high-fidelity simulators, significantly reducing the total number of grasps and their diversity. We propose a scalable generate-and-refine pipeline for synthesizing large-scale, diverse, and physically feasible grasps. Instead of using high-fidelity simulators solely for verification and filtering, we leverage them as an optimization stage that continuously improves grasp quality without discarding precomputed candidates. More specifically, we initialize an evolutionary search with a seed set of analytically generated, potentially suboptimal grasps. We then refine these proposals directly in a high-fidelity simulator (Isaac Sim) using an asynchronous, gradient-free evolutionary algorithm, improving stability while maintaining diversity. In addition, this refinement stage can be guided toward human preferences and/or domain-specific quality metrics without requiring a differentiable objective. We further distill the refined grasp distribution into a diffusion model for robust real-world deployment, and highlight the role of diversity for both effective training and during deployment. Experiments on a newly introduced Handles dataset and a DexGraspNet subset demonstrate that our approach achieves over 120 distinct stable grasps per object (a 1.7-6x improvement over unrefined analytical methods) while outperforming diffusion-based alternatives by 46-60\% in unique grasp coverage.

翻译：灵巧抓取是机器人学的基础，然而数据驱动的抓取预测严重依赖于大规模、多样化的数据集，这些数据集生成成本高昂，且通常局限于有限的夹持器形态。解析式抓取合成可用于扩展数据收集，但必要的简化假设常常产生物理上不可行的抓取，需要在高保真仿真器中进行筛选，这显著减少了抓取的总数及其多样性。我们提出了一种可扩展的生成-优化流程，用于合成大规模、多样化且物理可行的抓取。我们并非仅将高保真仿真器用于验证和筛选，而是将其作为优化阶段，持续提升抓取质量而不丢弃预计算的候选抓取。具体而言，我们使用一组解析生成、可能次优的抓取作为种子集来初始化进化搜索。随后，我们在高保真仿真器（Isaac Sim）中，采用异步、无梯度的进化算法直接优化这些提案，在保持多样性的同时提升稳定性。此外，该优化阶段可被引导至人类偏好和/或特定领域质量指标，而无需可微分的目标函数。我们进一步将优化后的抓取分布提炼为扩散模型，以实现鲁棒的实景部署，并强调了多样性在有效训练和部署期间的作用。在新引入的Handles数据集和DexGraspNet子集上的实验表明，我们的方法对每个物体实现了超过120个不同的稳定抓取（相比未优化的解析方法提升了1.7-6倍），同时在独特抓取覆盖率上优于基于扩散的替代方法46-60%。