让机器人手的外形和控制策略一起优化，1天内完成设计制造部署。
House of Dextra: Cross-embodied Co-design for Dexterous Hands
- 联合优化手部结构与控制算法，实现任务自适应
- 24小时内完成从设计到实机部署的全流程
- 支持真实世界应用，可使用常见零件快速制作
灵巧操作受限于控制与设计的协同不足，缺乏对最优操作器形态的共识。为此，我们提出一种联合设计框架，同时学习特定任务的手部形态与互补的灵巧控制策略。该框架支持：1）包含关节、手指与掌部生成的广泛形态搜索空间；2）通过形态条件化的跨本体控制实现大规模设计空间的高效评估；3）使用可获取组件实现真实世界制造。我们在多个灵巧任务中评估该方法，包括仿真与现实中的物体翻转操作。框架可实现端到端流程：从设计、训练、制造到部署，全程耗时不足24小时。完整框架及生成的手部模型已开源，可在官网获取。
原文摘要 · Abstract (English)
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 and generated robot hands are open-sourced and available on our website.
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