将手部结构与操控任务统一优化，实现更灵活的灵巧手设计。
Function-based Parametric Co-Design Optimization of Dexterous Hands
- 用参数化方法统一掌部、指节、指尖等多维度设计空间。
- 在仿真与真实动态场景中提升抓取稳定性，生成可制造模型。
- 适合做灵巧手设计、跨体态控制策略研究的开发者使用。
尽管灵巧手操作技术不断进步，机器人手的设计仍大多与任务评估和控制相分离，限制了系统性优化。现有协同设计方法通常范围有限，仅优化少数设计参数。本文提出一个全面的参数化框架，将手掌结构、手指运动学、指尖几何形状及微尺度表面曲率统一纳入单一设计空间。通过参数化表面变形核引入精细几何特征，直接影响接触交互。我们在仿真与真实世界的动态场景中验证了该框架在抓取稳定性任务上的优化效果。该框架可生成仿真与可制造的手部模型，并将开源发布，以支持灵巧手协同设计优化框架、跨体态策略训练与控制研究的快速迭代。
原文摘要 · Abstract (English)
Despite advances in dexterous hand manipulation, robotic hand design is still largely decoupled from task-driven evaluation and control, limiting systematic optimization. Existing robotic hand co-design approaches are often limited in scope, optimizing a small subset of design parameters. We introduce a comprehensive parametric framework for robotic hand generation that unifies palm structure, finger kinematics, fingertip geometry, and fine-scale surface curvatures within a single design space. Fine geometric features are introduced through parametric surface deformation kernels that directly influence contact interactions. We validate the framework on design optimization in grasp stability tasks in simulation and real-world dynamic scenarios. Our framework produces simulation- and fabrication-ready hand models and will be released as open-source to enable rapid design iteration for dexterous hand co-design optimization frameworks and cross-embodiment policy training and control research.
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