LabRobFail：化学自动驾驶实验室中机器人故障分析的基准
LabRobFail: A Benchmark for Robotic Failure Analysis in Chemical Self-driving Laboratory
AI总结：
针对化学自动驾驶实验室中机器人可靠性受化学实验特性限制、故障数据稀缺及评估协议缺乏等问题，引入LabRobFail框架，通过注入故障构建数据集，开发专门模型，提升故障检测等能力及下游任务成功率。
AI中文摘要：
在自动驾驶实验室中部署实体智能体可加速科学发现，但化学实验的不可逆性和安全关键性质限制了其可靠性。由于故障数据稀缺且缺乏细粒度评估协议，进展受阻。为此引入LabRobFail框架，LabRobFail-Sim在控制、物理和语义层面注入可控故障以构建包含超20000条轨迹的LabRobFail-Data。LabRobFail-Bench评估六种能力。还开发了LabRobFail-VLM，在可见环境中表现出色，集成后提高了下游VLA任务成功率，证明了细粒度故障理解对闭环恢复和可靠实验室自主性的价值。
英文摘要：
The deployment of embodied agents in self-driving laboratories could accelerate scientific discovery, yet their reliability is constrained by the irreversible and safety-critical nature of chemical experiments. Progress is further hindered by scarce failure data and the lack of fine-grained evaluation protocols. To address these challenges, we introduce LabRobFail, a failure-centric framework for learning and evaluating robotic failure analysis in chemical laboratories. LabRobFail-Sim injects controllable failures at the control, physics, and semantic levels, enabling the construction of LabRobFail-Data, which contains over 20,000 trajectories across 70+ task scenarios, five failure categories, and 11 fine-grained failure types. LabRobFail-Bench evaluates six capabilities spanning task understanding, failure detection, temporal localization, severity assessment, failure classification, and actionable correction. We further develop LabRobFail-VLM, a domain-specialized vision-language model that generates structured failure diagnoses and recovery instructions. On seen environments, it achieves 90.83% failure-detection accuracy and 77.21% temporal-localization accuracy, substantially outperforming general-purpose VLMs. When integrated as a real-time supervisor, it improves downstream task success rates by 4-16 percentage points, demonstrating the value of fine-grained failure understanding for closed-loop recovery and reliable laboratory autonomy. Our code and data are available at https://github.com/Su-ISE-2001/SciRobo
