arXiv：cs.LG· Rajneesh Anand, Mayuresh V. Kothare·· 4 小时前AI 评分35
基于模型的强化学习实现液滴自主导航：零样本迁移与涌现动力学
Autonomous Droplet Navigation via Model-Based Reinforcement Learning: Zero-Shot Transfer and Emergent Dynamics
AI 导读
研究人员推出首个用于开放无约束表面液滴闭环自主导航的机器人平台，采用基于模型的强化学习，由双轴倾斜板加硅油薄膜驱动液滴，顶部相机实时反馈。策略仅需 50 至 150 个物理回合训练，无需仿真或解析模型，可零样本迁移至未见几何结构，并能自主发现振荡脱钉策略解决液滴粘滞，完整训练流程在 90 分钟内完成。
正文
Abstract: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.
| Comments: | Accepted for presentation at the Robotics & Automation in Self-Driving Laboratories 2026 Workshop, IROS 2026. this https URL |
| Subjects: | Robotics (cs.RO); Machine Learning (cs.LG); Systems and Control (eess.SY) |
| Cite as: | arXiv:2610.08852 [cs.RO] |
| (or arXiv:2610.08852v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08852 arXiv-issued DOI via DataCite |
Submission history
From: Rajneesh Anand [view email]
[v1]
Sat, 3 Oct 2026 04:06:12 UTC (1,268 KB)
来源：arXiv：cs.LG · arxiv.org