Despite the critical role of bimanual manipulation in endowing robots with human-like dexterity, large-scale and diverse datasets remain scarce due to the significant hardware heterogeneity across bimanual robotic platforms. To bridge this gap, we introduce RoboCOIN, a large-scale multi-embodiment bimanual manipulation dataset comprising over 180,000 demonstrations collected from 15 distinct robotic platforms. Spanning 16 diverse environments-including residential, commercial, and industrial settings-the dataset features 421 bimanual tasks systematically categorized by 39 bimanual collaboration actions and 432 objects. A key innovation of our work is the hierarchical capability pyramid, which provides granular annotations ranging from trajectory-level concepts to segment-level subtasks and frame-level kinematics. Furthermore, we present CoRobot, an efficient data processing pipeline powered by the Robot Trajectory Markup Language (RTML), designed to facilitate quality assessment, automated annotation, and unified multi-embodiment and data management. Extensive experiments demonstrate the effectiveness of RoboCOIN in enhancing the performance of various bimanual manipulation models across a wide spectrum of robotic embodiments. The entire dataset and codebase are fully open-sourced, providing a valuable resource for advancing research in bimanual and multi-embodiment manipulation.

翻译：尽管双臂操控在赋予机器人类人灵巧性方面具有关键作用，但由于双臂机器人平台间显著的硬件异构性，大规模、多样化的数据集仍然稀缺。为填补这一空白，我们提出RoboCOIN——一个大规模多具身双臂操控数据集，包含从15个不同机器人平台采集的超过18万次演示数据。该数据集覆盖16种多样化环境（包括住宅、商业和工业场景），包含421项双臂任务，并基于39种双臂协作动作和432个物体进行系统分类。本研究的关键创新在于提出层次化能力金字塔体系，提供从轨迹级概念到片段级子任务、再到帧级运动学的精细标注。此外，我们提出CoRobot高效数据处理流水线，其核心为机器人轨迹标记语言（RTML），用于实现质量评估、自动标注及统一的多具身数据管理。大量实验证明，RoboCOIN在提升多种双臂操控模型于各类机器人具身上的性能方面具有显著效果。整个数据集与代码库均已完全开源，为推进双臂及多具身操控研究提供了宝贵资源。