用合成人类预抓取生成类人灵巧抓握，提升机器人操作成功率。
SynManDex: Synthesizing Human-like Dexterous Grasps from Synthetic Human Pre-Grasps
- 基于合成人类预抓取生成抓握提案，再通过机器人优化实现力闭合接触。
- 抓握稳定率达86.4%，类人度93.4%，仿真成功率80.7%。
- 适合需要高灵巧性抓握的机器人任务，如倒茶、拍照、吹笛等。
人类手物交互蕴含功能意图，但直接迁移至机器人常因形态、接触和可达性约束失败。我们提出SynManDex，一个合成管道：以生成的人类预抓取作为感知意识的候选，通过机器人本体优化解决最终接触问题。该方法采样条件化物体的数字人预抓取，将其重定向为灵巧机器人手姿态，在目标本体上优化力闭合接触，并仅保留每步验证通过的轨迹。生成的关键帧支持抓取-举升演示及多种前置操作任务（如倒茶、拍照、吹笛），由视觉语言模型（VLM）代理设计。结果表明，SynManDex在抓握质量（86.4%抓握稳定性）与类人度（4.67/5，93.4%）方面表现优异，在仿真中成功率达80.7%，应用于36自由度双臂灵巧机器人平台时，实机成功率为25/30（83.3%）。
原文摘要 · Abstract (English)
Human hand-object interactions encode functional intent, but direct transfer to robotic hands often fails under morphology, contact, and reachability constraints. We present SynManDex, a synthetic pipeline that uses generated human pre-grasps as affordance-aware proposals and resolves the final contacts with robot-native optimization. SynManDex samples object-conditioned digital human pre-grasps, retargets them to dexterous robotic hand poses, optimizes force-closure contacts on the target embodiment, and admits trajectories that pass checks from each step. The resulting keyframes support both grasp-and-lift demonstrations and various prehensile manipulation tasks such as tea pouring, photo taking, and flute playing, designed via VLM agents. As a result, SynManDex combines high grasp quality (86.4\% grasp stability) with 4.67/5 human-likeness (93.4\%). It achieves 80.7\% successes in simulation and 25/30 (83.3\%) real-robot successes when applied to a 36-DOF bimanual dexterous robotic platform.
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