SAFE-CHEM: Uncertainty-Aware Policy Switching for Robust Robotic Chemistry
2026-08-10 • Robotics
RoboticsMachine Learning
AI summaryⓘ
The authors created SAFE-CHEM, a system that helps robot chemists work safely in laboratories by knowing when they might make mistakes. They use multiple AI models to check how uncertain the robot is about its next action and switch to a safe backup plan if the risk is too high. This approach was tested on lab tasks and improved success while avoiding dangerous errors. They also showed it works on a real robot without extra training. Overall, their method helps robots safely learn and perform chemistry experiments.
autonomous roboticsimitation learningepistemic uncertaintyrecurrent neural networkskernel density estimationhybrid controlsim-to-real transferrobotic manipulationsafety in AImaterials chemistry
Authors
Laura Jones, Shazil Shahzad, Ayesha Sana, Gabriella Pizzuto
Abstract
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 robotic chemists. Our approach leverages an ensemble of recurrent neural network-based imitation learning policies to quantify epistemic uncertainty online through the variance of action predictions. By characterising the success-conditioned density of this variance using kernel density estimation, we introduce a hybrid control architecture that autonomously switches from the learned policy to a deterministic, rule-based backup controller when uncertainty exceeds a calibrated safety threshold. We evaluate SAFE-CHEM across three fundamental laboratory manipulation tasks, where our empirical results demonstrate that this hybrid strategy improves overall task success rates and reduces critical safety violations compared to traditional single-policy baselines. Finally, we demonstrate the practical viability of the framework through zero-shot sim-to-real transfer onto a physical Franka Production 3 robot manipulator.