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.

翻译：人手与物体的交互编码了功能性意图，但直接迁移到机械手常因形态、接触和可达性约束而失败。我们提出SynManDex，一个利用生成的人类预抓取作为可操作性提议、并通过机器人原生优化最终接触的合成流水线。SynManDex对物体条件化的数字人类预抓取进行采样，将其重定向至灵巧机械手姿态，在目标实体上优化力闭合接触，并接受通过各步骤检查的轨迹。生成的关建帧支持抓取-提升演示以及多种抓取操控任务（如倒茶、拍照和吹笛子），这些任务通过VLM智能体设计。实验表明，SynManDex在实现高抓取质量（86.4%抓取稳定性）的同时，达到了4.67/5的类人度（93.4%）。其在仿真中成功率为80.7%，在应用于36自由度双臂灵巧机器人平台时，真实机器人成功率达25/30（83.3%）。