Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss
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Abstract (arXiv)
Optimal path planning and control of microscopic devices navigating in fluid environments is essential for applications ranging from targeted drug delivery to environmental monitoring. These tasks are challenging due to the complexity of microdevice-flow interactions. We introduce a closed-loop control method that optimizes a discrete loss (ODIL) in terms of dynamics and path objectives. In comparison with reinforcement learning, ODIL is more robust, up to three orders faster, and excels in high-dimensional action/state spaces, making it a powerful tool for navigating complex flow environments.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Jun 2025.
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025Contactless Precision Steering of Particles in a Fluid inside a Cube with Rotating Wallscited by 1
- 2025Bayesian Inference for PDE-based Inverse Problems using the Optimization of a Discrete Losscited by 1
- 2025Controlling Topological Defects in Polar Fluids via Reinforcement Learningcited by 0
- 2026Physics-enhanced reinforcement learning for real-time optimal control of dynamical systemscited by 0
- 2026Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems17 Jul 2026
- 2025Bayesian Inference for PDE-based Inverse Problems using the Optimization of a Discrete Loss17 Oct 2025
- 2025Controlling Topological Defects in Polar Fluids via Reinforcement Learning25 Jul 2025
- 2025Contactless Precision Steering of Particles in a Fluid inside a Cube with Rotating Walls19 Jun 2025
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- 2024Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning
- 2026Autonomous Droplet Navigation via Model-Based Reinforcement Learning
- 2026Micro-Swarm Locomotion Optimization in Dynamic Flow using Multi-Objective Multi-Agent Reinforcement Learning ↗
- 2026Flow-aware Optimal Navigation in Unsteady Flows through Reinforcement Learning
- 2026Autonomous Droplet Navigation via Model-Based Reinforcement Learning: Zero-Shot Transfer and Emergent Dynamics