Bimanual manipulation, i.e., the coordinated use of two robotic arms to complete tasks, is essential for achieving human-level dexterity in robotics. Recent simulation benchmarks, e.g., RoboTwin and RLBench2, have advanced data-driven learning for bimanual manipulation. However, existing tasks are short-horizon and only loosely coordinated, failing to capture the spatial-temporal coupling inherent in real-world bimanual behaviors. To address this gap, we introduce BiCoord, a benchmark for long-horizon and tightly coordinated bimanual manipulation. Specifically, BiCoord comprises diverse tasks that require continuous inter-arm dependency and dynamic role exchange across multiple sub-goals. Also, we propose a suite of quantitative metrics that evaluate coordination from temporal, spatial, and spatial-temporal perspectives, enabling systematic measurement of bimanual cooperation. Experimental results show that representative manipulation policies, e.g., DP, RDT, Pi0, and OpenVLA-OFT, struggle with long-duration and highly coupled tasks, revealing fundamental challenges in achieving long-horizon and tight coordination tasks. We hope BiCoord can serve as a foundation for studying long-horizon cooperative manipulation and inspire future research on coordination-aware robotic learning. All datasets, codes and supplements could be found at https://buaa-colalab.github.io/BiCoord/.

翻译：双臂操作，即协调使用两个机械臂完成操作任务，是实现机器人与人级灵巧操作能力的关键。近期，诸如RoboTwin和RLBench2等仿真基准推动了双臂操作的数据驱动学习发展。然而，现有任务多为短期且仅具备松散协调性，未能捕捉真实世界双臂行为中固有的时空耦合特性。为弥补这一不足，我们提出BiCoord——一个面向长期且紧密协调的双臂操作基准。具体而言，BiCoord包含多样化的任务，这些任务要求连续的手臂间依赖关系以及跨多个子目标的动态角色交换。同时，我们提出了一套定量评估指标，从时间、空间和时空三个维度评估协调性，从而实现对双臂协作的系统性度量。实验结果表明，代表性的操作策略（如DP、RDT、Pi0和OpenVLA-OFT）在处理长时长且高度耦合的任务时表现不佳，揭示了实现长期且紧密协调任务所面临的根本性挑战。我们希望BiCoord能够作为研究长期协作操作的基石，并启发未来关于协调感知型机器人学习的研究。所有数据集、代码及补充材料均可在 https://buaa-colalab.github.io/BiCoord/ 获取。