Function-based Parametric Co-Design Optimization of Dexterous Hands

TL;DR

This paper presents a comprehensive parametric framework for robotic hand design that unifies various structural and geometric features, enabling systematic optimization for grasping tasks.

Contribution

It introduces a unified parametric design space for robotic hands, including surface deformation kernels, and validates it through grasp stability optimization in simulation and real-world scenarios.

Findings

Framework produces simulation- and fabrication-ready hand models.

Validated on grasp stability tasks in simulation and real-world.

Open-source release to facilitate rapid design and control research.

Abstract

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.