强化学习让微机器人在模拟血管中自主导航并干预血流。
Reinforcement Learning Enables Autonomous Microrobot Navigation and Intervention in Simulated Blood Capillaries
- 用物理真实仿真训练深度强化学习代理，实现微机器人自主导航。
- 发现布朗运动与血流会抑制推进力的禁区，影响机器人尺寸和速度选择。
- 策略通用性强，无需重训即可完成堵塞疏通，适合生物医学应用。
自主微机器人在生物血管中导航可实现靶向给药和溶栓治疗，但真实环境下的控制策略训练仍具挑战。以往强化学习研究多限于理想化几何结构，未包含复杂的流体动力学、分支结构和密集细胞障碍。本文构建了基于真实血流场、显式红细胞动态及解剖学分支结构的物理仿真系统，通过化学趋性训练深度强化学习代理进行导航。系统性地揭示了机器人尺寸与游动速度对导航能力的物理极限，识别出布朗运动与流体阻力超过推进力的禁行区域。成功训练的智能体独立发现多种通用策略，如跑-转模式和节能搜寻-驻留策略，且不依赖机器人参数。无需重新训练，这些智能体即可实现靶向阻塞与解阻，恢复至健康血流水平。结果表明，强化学习是开发复杂生物环境中自主微机器人干预策略的可行框架。
原文摘要 · Abstract (English)
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.
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