发表机构
Vrije Universiteit Amsterdam; University of Liverpool（阿姆斯特丹自由大学; 利物浦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对实验室自动化中粉末称量瓶颈，提出工具-策略协同设计框架，通过双层优化同时优化工具形状与控制策略，在相同计算预算下探索更多配置，并将实际称量误差降低45%。
AI 中文摘要
自主粉末称量是实验室自动化中的众多瓶颈之一，原因在于异质材料具有复杂、非线性的动力学特性。执行此任务的机器人化学家使用为人类手部灵巧性而设计的标准工具，这些工具的固定几何形状设定了控制策略需要调节的动力学。本工作引入了一个工具-策略协同设计框架，该框架同时优化分配工具的形状及其控制策略，以供化学实验室中的机器人使用，其形式化为一个双层优化问题，旨在最小化目标粉末流动性分布上的分配误差。外层循环使用贝叶斯优化和Hyperband算法改变工具设计参数，如工具深度、宽度和边缘尖峰拓扑，而内层循环则为每个候选形状优化控制策略。我们还引入了一个几何相似性度量，该度量从结构相似设计的缓存策略中热启动策略训练，在相同的计算预算下探索了28%更多的配置。所提出的框架在一个流动感知的机器人-材料模拟框架中，针对具有不同物理动力学的七种材料，在机器人粉末称量任务上进行了评估。实验结果表明，与标准工具相比，我们协同设计的工具形状将实际称量误差降低了45%，包括对先前未见过的材料。这些结果表明，我们的方法能够同时调整控制策略和物理工具以适应目标材料的动力学，为实验室自动化领域带来了材料操作的新范式。
英文摘要
Autonomous powder weighing is one of many bottlenecks in laboratory automation due to the complex, non-linear dynamics of heterogeneous materials. Robot chemists performing this task utilise standard tools shaped for the dexterity of human hands, whose fixed geometry sets the dynamics that the control policy needs to regulate. This work introduces a tool-policy co-design framework that concurrently optimises the morphology of a dispensing tool and its control policy for use by robots in chemistry laboratories, formulated as a bi-level optimisation that minimises dispensing error over a target distribution of powder flowabilities. The outer loop varies tool-design parameters such as tool depth, width and rim spike topology using Bayesian optimisation and hyperband, while an inner loop optimises a control policy for each candidate morphology. We also introduce a geometric similarity metric that warm-starts policy training from cached policies of structurally similar designs, exploring 28% more configurations under the same compute budget. The proposed framework is evaluated on a robotic powder weighing task across seven materials with distinct physical dynamics in a flowability-informed robot-material simulation framework. Experimental results demonstrate that our co-designed tool morphology reduces real-world weighing errors by 45% relative to a standard tool, including on previously unseen materials. These results demonstrate our method can adapt both the control policy and the physical tool to the dynamics of the target material, bringing a new paradigm for material manipulation to the field of laboratory automation.
CommentsPaper video can be found at https://youtu.be/BEUT70hX9LM
