LabDex：面向实验室灵巧操作的分层基准
LabDex: A Hierarchical Benchmark for Dexterous Manipulation in Laboratories
中文总结 AI 辅助
LabDex是面向化学实验室灵巧操作的分层基准，整合真实与仿真平台，覆盖原子技能、组合任务及长周期实验，可用于机器人学习方法的跨层级评估，为自主实验室机器人研发提供支撑。
中文摘要 AI 辅助
自主实验室有望大幅加速科学发现。要实现这一愿景，机器人需灵巧操作各类实验室器具与仪器，执行长周期、依赖状态的实验流程。但现有基准无法同时覆盖灵巧手操作、真实实验室交互及多阶段实验流程，限制了系统训练与评估。为填补这一空白，我们推出LabDex——面向化学实验室灵巧操作的大规模真实数据集与基准，围绕分层任务分类体系组织，涵盖原子技能、组合任务及长周期实验。首先，LabDex支持跨平台，首次在统一框架下整合真实与仿真平台，提供标准化任务定义、演示及评估协议。其次，LabDex规模庞大，将化学实验室操作系统组织为三个相互关联的层级：表征基础灵巧操作能力的原子技能、组合技能，以及长周期实验室工作流。该分层设计不仅支持最终任务性能评估，还可分析基础灵巧技能如何组合并影响更复杂的实验室操作。我们在真实与仿真环境中对代表性机器人学习方法开展跨层级评估。实验结果验证了LabDex任务设计与演示数据的有效性，表明该基准支持现有机器人策略在不同层级实验室灵巧操作任务上的训练与系统评估，为自主实验室机器人的进一步研发奠定了基础。
英文摘要
Autonomous laboratories hold great promise for accelerating scientific discovery. To achieve this vision, robots are supposed to dexterously manipulate diverse labware and instruments and execute long-horizon, state-dependent experimental procedures. Yet existing benchmarks do not jointly capture dexterous hand use, real-world laboratory interactions, and multi-stage experimental procedures, limiting systematic training and evaluation. To bridge this gap, we introduce LabDex, a large-scale real-world dataset and benchmark for dexterous manipulation in chemistry laboratories, organized around a hierarchical task taxonomy spanning atomic skills, compositional tasks, and long-horizon experiments. First, LabDex is cross-platform and, for the first time, unifies real-world and simulation platforms under a common framework, providing standardized task definitions, demonstrations, and evaluation protocols. Second, LabDex is large-scale and systematically organizes chemistry laboratory operations into three interconnected levels: Atomic Skills, which characterize fundamental dexterous manipulation capabilities; Compositional Skills; and Long-Horizon Laboratory Workflows. This hierarchical design not only supports the evaluation of end-task performance, but also enables the analysis of how fundamental dexterous skills compose and influence more complex laboratory operations. We conduct cross-level evaluations of representative robot learning methods in both real-world and simulation environments. The experimental results validate the effectiveness of the LabDex task design and demonstration data, and show that the benchmark supports the training and systematic evaluation of existing robotic policies across laboratory dexterous manipulation tasks at different levels, providing a foundation for further research and development of autonomous laboratory robots.
