We introduce Yielding Universal Bidigital Interface (YUBI), a finger-aligned gripper designed to enable intuitive, ergonomic, and scalable data collection for bimanual dexterous manipulation. While handheld data collection systems such as Universal Manipulation Interface (UMI) enable affordable data collection, their bulky pistol-grip designs can pose ergonomic and usability challenges for fine-grained, dexterous manipulation tasks. To address this, YUBI presents a distinct design principle: yielding, finger-driven actuation that directly maps human finger movements to gripper jaw motion. Using the YUBI devices, we set up a data collection system with integrated VR-based 6 DoF tracking of the gripper, ensuring high-fidelity trajectory data acquisition. We curate a UMI-based dataset of unprecedented scale: 8,434 hours across 1.20M episodes and 119 tasks. Experiments show that YUBI offers advantages over the UMI gripper in versatility for complex bimanual tasks, dexterity, and operational efficiency. A single policy trained on the YUBI dataset transfers across multiple bimanual robots (UR, Franka, and ELEY) simply by mounting the gripper on each platform, confirming that the collected data are directly executable as policy supervision. We release the gripper hardware, data-collection software, and dataset as one integrated stack, offering the open community a reproducible path to large-scale data acquisition for advancing robotic foundation models.

翻译：我们提出了“通用双指接口”（Yielding Universal Bidigital Interface，YUBI），一种与手指对齐的夹持器，旨在实现直观、人体工学且可扩展的双手灵巧操作数据采集。尽管手持式数据采集系统（如通用操作界面UMI）能够实现低成本数据采集，但其笨重的枪式握把设计在精细灵巧操作任务中可能存在人体工学与可用性挑战。为此，YUBI提出了一种独特的设计原则：采用顺从式手指驱动机制，直接将人类手指运动映射至夹持器颚部动作。利用YUBI装置，我们构建了集成基于虚拟现实的六自由度夹持器追踪的数据采集系统，确保了高保真轨迹数据获取。我们整理了一个规模空前的基于UMI的数据集：涵盖1,8434小时、120万条轨迹片段及119项任务。实验表明，YUBI在复杂双手任务的通用性、灵巧性及操作效率方面均优于UMI夹持器。基于YUBI数据集训练的单一策略，仅需将夹持器安装至各平台即可直接迁移至多种双手机器人系统（UR、Franka和ELEY），证实所采集数据可作为策略监督信号直接执行。我们发布了夹持器硬件、数据采集软件及数据集作为一体化工具包，为开放社区提供可复现的大规模数据采集路径，以推动机器人基础模型的发展。