Dexterous manipulation is limited by both control and design, without consensus as to what makes manipulators best for performing dexterous tasks. This raises a fundamental challenge: how should we design and control robot manipulators that are optimized for dexterity? We present a co-design framework that learns task-specific hand morphology and complementary dexterous control policies. The framework supports 1) an expansive morphology search space including joint, finger, and palm generation, 2) scalable evaluation across the wide design space via morphology-conditioned cross-embodied control, and 3) real-world fabrication with accessible components. We evaluate the approach across multiple dexterous tasks, including in-hand rotation with simulation and real deployment. Our framework enables an end-to-end pipeline that can design, train, fabricate, and deploy a new robotic hand in under 24 hours. The full framework will be open-sourced and available on our website.

翻译：灵巧操作受限于控制与设计两方面，且对于何种机械手最适合执行灵巧任务尚未形成共识。这提出了一个根本性挑战：我们应如何设计与控制针对灵巧性优化的机器人机械手？本文提出一种协同设计框架，能够学习任务特定的手部形态及与之互补的灵巧控制策略。该框架支持：1）涵盖关节、手指与手掌生成的广阔形态搜索空间；2）通过形态条件化的跨具身控制实现广泛设计空间的可扩展评估；3）使用易获取组件实现实物制造。我们在多项灵巧任务中评估该方法，包括仿真与真实部署环境下的手内旋转任务。本框架实现了一个端到端流程，可在24小时内完成新型机器人手的设计、训练、制造与部署。完整框架将在我们的网站上开源提供。