mlsanityData to 7 Oct 2026

Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning

arXiv 2404.02171 ↗First posted 29 Mar 2024physics.bio-ph cs.LG cs.ROcited by 1

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Abstract (arXiv)

Biomedical applications such as targeted drug delivery, microsurgery, and sensing rely on reaching precise areas within the body in a minimally invasive way. Artificial bacterial flagella (ABFs) have emerged as potential tools for this task by navigating through the circulatory system with the help of external magnetic fields. While their swimming characteristics are well understood in simple settings, their controlled navigation through realistic capillary networks remains a significant challenge due to the complexity of blood flow and the high computational cost of detailed simulations. We address this challenge by conducting numerical simulations of ABFs in retinal capillaries, propelled by an external magnetic field. The simulations are based on a validated blood model that predicts the dynamics of individual red blood cells and their hydrodynamic interactions with ABFs. The magnetic field follows a control policy that brings the ABF to a prescribed target. The control policy is learned with an actor-critic, off-policy reinforcement learning algorithm coupled with a reduced-order model of the system. We show that the same policy robustly guides the ABF to a prescribed target in both the reduced-order model and the fine-grained blood simulations. This approach is suitable for designing robust control policies for personalized medicine at moderate computational cost.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 57 references that are in the map. First posted on arXiv 29 Mar 2024.

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12 of 57 references.

References · 12

  1. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 616
  2. 2018Efficient collective swimming by harnessing vortices through deep reinforcement learningcited by 73
  3. 2018Remember and Forget for Experience Replaycited by 42
  4. 2017Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimizationcited by 33
  5. 2017Flow Navigation by Smart Microswimmers via Reinforcement Learningcited by 32
  6. 2019Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using Reinforcement Learningcited by 21
  7. 2019Machine learning strategies for path-planning microswimmers in turbulent flowscited by 13
  8. 2021Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds numbercited by 7
  9. 2020Korali: Efficient and Scalable Software Framework for Bayesian Uncertainty Quantification and Stochastic Optimizationcited by 4
  10. 2021Learning swimming escape patterns for larval fish under energy constraintscited by 3
  11. 2023The stress-free state of human erythrocytes: data driven inference of a transferable RBC modelcited by 2
  12. 2019Mirheo: High-Performance Mesoscale Simulations for Microfluidicscited by 1

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