发表机构
Lehigh University（里海大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用基于模型的强化学习，在重力驱动平台上实现了液滴在复杂几何结构中的自主导航，策略从有限数据中学习，并成功迁移到新路径，为智能微流控化学实验室奠定基础。
AI 中文摘要
液体液滴的精确操控是用于诊断、化学合成和生物检测的芯片实验室平台的基础。然而，在复杂程度不同的受限几何结构中实现自主液滴输运仍然是一个未解决的挑战。液滴表现出接触角滞后、可变形性和毛细管钉扎，这使得它们对驱动的响应是非线性的且具有历史依赖性，经典控制器和预编程轨迹在多转弯环境中无法应对。在这里，我们展示了在重力驱动的（迷宫）平台上，使用基于模型的强化学习，实现液滴在复杂度递增的几何结构中的自主导航。一层薄薄的硅油膜减少了接触线钉扎，而双轴倾斜提供重力驱动力，并且一个头顶摄像头实时跟踪液滴。一个离线训练的策略从有限的物理交互数据中发现了有效的倾斜策略，无需模拟或解析液滴模型。该系统在部分可观测性下运行，因为油膜厚度、瞬时接触角和液滴变形状态对控制器是隐藏的。尽管存在这些挑战，学习到的策略在直线、直角和弧形路径（包括外角几何结构）上实现了可靠的导航。我们进一步证明，在较简单几何结构上训练的策略可以迁移到复杂几何结构，在直角和阶梯路径上实现零样本成功，并且在弧形路径上仅用五分之一训练数据就达到完全成功。这些发现为使基于液滴的微流控系统成为智能化学实验室提供了有前景的途径。
英文摘要
Precise manipulation of liquid droplets underpins lab-on-a-chip platforms for diagnostics, chemical synthesis, and biological assays. Yet autonomous droplet transport through confined geometries of varying complexity remains an open challenge. Droplets exhibit contact-angle hysteresis, deformability, and capillary pinning, which make their response to actuation nonlinear and history dependent, that classical controllers and pre-programmed trajectories cannot cope in multi-turn environments. Here we demonstrate autonomous navigation of a liquid droplet through geometries of increasing complexity on a gravity driven (Labyrinth) platform using model-based reinforcement learning. A thin silicone oil film reduces contact-line pinning while two-axis tilt supplies the gravitational driving force, and an overhead camera tracks the droplet in real time. An offline-trained policy discovers effective tilt strategies from limited physical interaction data, without simulation or analytical droplet models. The system operates under partial observability, as oil-film thickness, instantaneous contact angle, and droplet deformation state remain hidden from the controller. Despite these challenges, the learned policy achieves reliable navigation across straight, right-angle, and curved-arc paths, including outside-corner geometries. We further demonstrate that a policy trained on a simpler geometry transfers to complex ones, succeeding zero-shot on right-angle and staircase paths and reaching full success on a curved arc with a fifth of the training data. The findings suggest promising avenues for enabling droplet based microfluidic systems to serve as intelligent chemical laboratories.
Comments43 pages, 15 figures, 3 tables including supplementary material. The source code is available via GitHub at https://github.com/rajneeshanand/DropletRunner. An archived version of all supplementary movies has also been uploaded to Google Drive: https://drive.google.com/drive/folders/1ewK2dxWxfzjd4kk6Df4cqKE3-3xCbOgf?usp=sharing
