通过软硬协同设计，让机械手同时实现稳定抓握与精细操作。
From Power to Precision: Learning Fine-grained Dexterity for Multi-fingered Robotic Hands
- 联合优化控制策略与指尖结构，动态切换抓握模式。
- 仿真到真实任务中精度抓取零样本成功率82.5%，面包夹取成功率93.3%。
- 无需重造机械手，轻量改造即可提升灵巧性，适合工业抓取场景。
人类抓握可分为力量型与精细型两类，后者支撑工具使用并影响进化。当前多指机械手在力量抓握上表现良好，但精细操作仍依赖平行夹爪。本文通过软硬协同设计，使机械手兼具稳定抓握与精细操控能力。不重新设计整手，而是引入轻量化指尖几何修改，并以接触面表示，联合优化其参数与控制策略。控制策略可动态切换抓握模式，将精细操作简化为拇指与食指的平行运动，具备良好的仿真到现实迁移能力。设计方面，利用大规模仿真和可微神经物理模型优化指尖形状。实验验证表明，在仿真到真实场景中对未见过物体的精细抓取零样本成功率达82.5%，真实世界面包夹取任务成功率93.3%。结果证明，该协同设计框架能显著提升多指手的精细操作能力，同时保持其力量抓握性能。
原文摘要 · Abstract (English)
Human grasps can be roughly categorized into two types: power grasps and precision grasps. Precision grasping enables tool use and is believed to have influenced human evolution. Today's multi-fingered robotic hands are effective in power grasps, but for tasks requiring precision, parallel grippers are still more widely adopted. This contrast highlights a key limitation in current robotic hand design: the difficulty of achieving both stable power grasps and precise, fine-grained manipulation within a single, versatile system. In this work, we bridge this gap by jointly optimizing the control and hardware design of a multi-fingered dexterous hand, enabling both power and precision manipulation. Rather than redesigning the entire hand, we introduce a lightweight fingertip geometry modification, represent it as a contact plane, and jointly optimize its parameters along with the corresponding control. Our control strategy dynamically switches between power and precision manipulation and simplifies precision control into parallel thumb-index motions, which proves robust for sim-to-real transfer. On the design side, we leverage large-scale simulation to optimize the fingertip geometry using a differentiable neural-physics surrogate model. We validate our approach through extensive experiments in both sim-to-real and real-to-real settings. Our method achieves an 82.5% zero-shot success rate on unseen objects in sim-to-real precision grasping, and a 93.3% success rate in challenging real-world tasks involving bread pinching. These results demonstrate that our co-design framework can significantly enhance the fine-grained manipulation ability of multi-fingered hands without reducing their ability for power grasps. Our project page is at https://jianglongye.com/power-to-precision
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