arXiv 2602.152012026-02-16cs.RO

DexEvolve: 为鲁棒且多样化的灵活抓取合成设计的进化优化

DexEvolve: Evolutionary Optimization for Robust and Diverse Dexterous Grasp Synthesis

René Zurbrügg, Andrei Cramariuc, Marco Hutter

AI总结：

DexEvolve通过进化优化生成多样且物理可行的抓取，提升现实部署的鲁棒性。

摘要：

灵活抓取是机器人技术中的基本要素，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，这些数据集的生成成本高且通常仅限于狭窄的夹具形态集合。分析性抓取合成可以用于扩大数据收集，但必要的简化假设往往会产生物理上不可行的抓取，需要在高保真模拟器中进行过滤，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与优化流程，用于合成大规模、多样且物理上可行的抓取。与仅将高保真模拟器用于验证和过滤不同，我们利用它们作为优化阶段，持续改进抓取质量而不丢弃预计算的候选方案。更具体地说，我们以一组分析性生成的、可能次优的抓取作为进化搜索的初始设置。然后，我们直接在高保真模拟器（Isaac Sim）中对这些提案进行优化，使用异步、无梯度的进化算法，提高稳定性的同时保持多样性。此外，这一优化阶段可以引导至人类偏好和/或领域特定的质量指标，而无需可微目标。我们进一步将优化后的抓取分布提炼成扩散模型，以实现稳健的现实部署，并强调多样性在有效训练和部署中的作用。在新引入的Handles数据集和DexGraspNet子集中进行的实验表明，我们的方法在每个物体上实现了超过120种不同的稳定抓取（比未经优化的分析方法提高了1.7至6倍），同时在独特的抓取覆盖方面优于基于扩散的方法，提高了46-60%。

英文摘要：

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.

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