# Reinforcement Learning Enables Autonomous Microrobot Navigation and Intervention in Simulated Blood Capillaries

arXiv: https://arxiv.org/abs/2606.26154 · HTML: https://mlsanity.com/paper/2606.26154/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 64 references that are in the map.
- First posted on arXiv 23 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Flow Navigation by Smart Microswimmers via Reinforcement Learning](https://mlsanity.com/paper/1701.08848/) (2017)
- [Reinforcement learning of optimal active particle navigation](https://mlsanity.com/paper/2202.00812/) (2022)
- [waLBerla: A block-structured high-performance framework for multiphysics simulations](https://mlsanity.com/paper/1909.13772/) (2019)
- [Reinforcement Learning for Active Matter](https://mlsanity.com/paper/2503.23308/) (2025)
- [Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning](https://mlsanity.com/paper/2307.00994/) (2023)
- [Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2404.01999/) (2024)
- [SwarmRL: Building the Future of Smart Active Systems](https://mlsanity.com/paper/2404.16388/) (2024)
- [Clinically Ready Magnetic Microrobots for Targeted Therapies](https://mlsanity.com/paper/2501.11553/) (2025)
- [Microscopic Robots That Sense, Think, Act, and Compute](https://mlsanity.com/paper/2503.23085/) (2025)

## Abstract (arXiv)

Autonomous microrobots navigating biological vasculature could enable targeted drug delivery and thrombolysis, yet training control policies for realistic environments remains an open challenge. Prior reinforcement learning (RL) studies of microrobotic navigation have been limited to idealized geometries that omit complex hydrodynamic flow fields, confined branching structures, and dense cellular obstacles found in vivo. Here, we develop a physically grounded simulation of a blood capillary network, incorporating realistic hydrodynamic flow fields, explicit red blood cell dynamics, and anatomically derived branching geometry, and train deep RL agents to navigate it via chemotaxis. We systematically map the physical limits of navigation across robot size and swimming speed, revealing a forbidden regime where Brownian motion and flow overcome propulsion. Successful agents independently discover multiple universal strategy types, including run-and-rotate and energy-efficient search-and-sit policies, regardless of robot parameters. Without retraining, these agents perform targeted blocking and unblocking of capillary flow, restoring throughput to healthy baseline levels. These results establish RL as a viable framework for developing autonomous microrobotic intervention strategies in complex biological environments.