SOURCE-LINKED INTELLIGENCE
Autonomous Droplet Navigation via Model-Based Reinforcement Learning
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 complexit
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T21:31:28.000Z
First collected: 2026-09-20T09:01:24.920Z. This is not the publication date.