发表机构
Lehigh University（理海大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出首个基于模型强化学习的自主液滴导航机器人平台，实现零样本迁移、振荡去钉扎策略及快速训练，将操控扩展至软物质系统。
AI 中文摘要
自驱动实验室（SDLs）正通过闭环自动化变革化学与材料发现过程，然而，用于软质、可变形物质物理操作的自动化基础设施仍超出当前机器人平台的能力范围。一个关键实例是开放表面上的自主液滴传输，其中接触角滞后、毛细管钉扎和表面异质性产生部分可观测的动力学，这对经典基于模型的控制器构成重大挑战。我们引入了首个利用基于模型的强化学习在开放、无约束表面上进行闭环自主液滴导航的机器人平台。一个涂有薄硅油膜的双轴倾斜板驱动液滴，同时一个顶置摄像头提供实时反馈。根据几何复杂度，学习策略仅需50至150个物理回合即可训练完成，无需仿真或解析模型。除了性能之外，该平台还展示了SDL社区感兴趣的三种能力：它能稳健地零样本迁移到未见过的几何形状；它能自主发现一种振荡去钉扎策略，在液滴粘附时将其释放；并且它能在90分钟内完成其完整的训练流程。这些结果将强化学习操控从刚性微型机器人扩展到可变形软物质系统，为下一代SDLs铺平道路。
英文摘要
Self-driving laboratories (SDLs) are transforming chemical and materials discovery through closed-loop automation, yet automated infrastructure for physical manipulation of soft, deformable matter remains beyond current robotic platforms. A critical instance is autonomous droplet transport on an open surface, where contact-angle hysteresis, capillary pinning, and surface heterogeneity produce partially observable dynamics that pose significant challenges for classical model-based controllers. We introduce the first robotic platform for closed-loop autonomous liquid droplet navigation on an open, unconfined surface using model-based reinforcement learning. A two-axis tilting board coated with a thin silicone oil film drives the droplet, while an overhead camera provides real-time feedback. A learned policy was trained on just 50 to 150 physical episodes depending on geometric complexity, without simulation or analytical models. Beyond performance alone, the platform demonstrates three capabilities of interest to the SDL community: it robustly transfers zero-shot to unseen geometries; it autonomously discovers an oscillatory depinning strategy to free the droplet when it sticks; and it completes its full training pipeline in under 90 minutes. These results extend reinforcement-learning manipulation from rigid microrobots to deformable soft-matter systems for next-generation SDLs.
CommentsAccepted for presentation at the Robotics & Automation in Self-Driving Laboratories 2026 Workshop, IROS 2026. https://sites.tufts.edu/selfdrivinglabs-iros2026/
