Learning dexterous manipulation requires demonstrations that preserve fine hand-object interactions while remaining executable at deployment. Existing pipelines either lose deployable dexterity through retargeting or embodiment conversion, or rely on robot-specific teleoperation that is costly to scale and often lacks intuitive, contact-aware control for dexterous data collection. We present RealDexUMI, a wearable universal manipulation interface built around a shared dexterous end-effector module that integrates a lightweight dexterous hand, in-hand vision, and fingertip tactile sensing. A palm-side isomorphic teleoperation glove maps human finger inputs to robot-hand joint commands, enabling real-time, retargeting-free, intuitive, and precise hand control. The shared hand and sensing modules yield zero-gap end-effector data, with matched in-hand observations, tactile signals, contacts, and hand actions between collection and deployment. Across eight real-robot tasks spanning fine-grained, contact-rich, long-horizon, and bimanual manipulation, policies trained on RealDexUMI data achieve an average success rate of 88.75%, generalize to unseen initial poses, and transfer across three embodiments. Website: https://research.beingbeyond.com/realdexumi

翻译：学习灵巧操作需要既能保留精细手-物交互、又在部署时保持可执行性的示范数据。现有流程或通过重定向或具身转换损失了可部署灵巧性，或依赖代价高昂且难以规模化扩展的机器人专用遥操作，且通常缺乏用于灵巧数据收集的直观、接触感知控制能力。本文提出RealDexUMI——一种基于共享灵巧末端执行器模块构建的可穿戴通用操作接口，该模块集成了轻量级灵巧手、手内视觉与指尖触觉传感。掌侧同构遥操作手套将人类手指输入映射至机器人手部关节指令，实现了实时、免重定向、直观且精确的手部控制。共享手部与传感模块产生零间隙的末端执行器数据，在采集与部署阶段提供匹配的手内观测、触觉信号、接触信息及手部动作。在涵盖精细操作、接触密集任务、长时域操作及双臂操作的八项真实机器人任务中，基于RealDexUMI数据训练的策略实现了88.75%的平均成功率，可泛化至未见初始位姿，并能在三种具身形态间迁移。网站：https://research.beingbeyond.com/realdexumi