SAFE-CHEM: Uncertainty-Aware Policy Switching for Robust Robotic Chemistry

• URL: http://arxiv.org/abs/2608.09303v1
• Date: Mon, 10 Aug 2026 08:51:43 GMT
• Title: SAFE-CHEM: Uncertainty-Aware Policy Switching for Robust Robotic Chemistry
• Abstract summary: SAFE-CHEM is an uncertainty-aware framework designed for robust, learning-based robotic chemists.<n>Our approach leverages an ensemble of recurrent neural network-based imitation learning policies to quantify uncertainty online.<n>We introduce a hybrid control architecture that autonomously switches from the learned policy to a deterministic, rule-based backup controller.
• Score: 0.47248250311484113
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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