SAFE-CHEM: Uncertainty-Aware Policy Switching for Robust Robotic Chemistry
The deployment of autonomous robotic systems in chemistry laboratories is accelerating experimental workflows and providing the foundational data for AI-driven scientific discovery. However, despite the success of data-driven methods in acquiring dexterous skills, safety remains a primary barrier to their deployment in high-risk domains, such as early-stage materials chemistry experiments. Specifically, learning-based policies frequently struggle to distinguish between safe and unsafe actions, leading to overconfident extrapolation and potentially catastrophic failures. To mitigate these safety risks, we propose SAFE-CHEM, an uncertainty-aware framework designed for robust, learning-based...
| Authors | Laura Jones, Shazil Shahzad, Ayesha Sana et al. |
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| Primary category | cs.RO |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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