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About this paper
Haobo Wang, Baoli Sun, Anqi Zou, Dongsheng Huang, Zelin Lv, Ning Wang, Rui Li, Dongzhan Zhou and 3 more
LabRobFail introduces a comprehensive framework designed for robotic failure analysis within chemical self-driving laboratories, encompassing a simulation platform for systematic error generation, a large-scale dataset, and a multi-dimensional benchmark. A domain-specialized Vision-Language Model developed within this framework achieves 90.83% accuracy in failure detection and 77.21% in temporal localization, notably improving downstream task success rates by 4-16 percentage points.
Key insights
- Systematic, multi-level failure injection across control, physics, and semantic aspects enables the generation of diverse and comprehensively annotated failure trajectories crucial for training robust robotic agents.
- Domain-specialized Vision-Language Models, leveraging hybrid fine-tuning and spatio-temporal input representations, demonstrably outperform generalist models in precise failure analysis and recovery planning within safety-critical laboratory settings.
- Evaluating robotic agents on fine-grained metrics like temporal localization, severity assessment, and actionable correction planning is essential for reliable autonomy in irreversible chemical experiments, contrasting with coarser existing benchmarks.
Summary by alphaXiv.
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