Reinforcement Learning Enables Autonomous Microrobot Navigation and Intervention in Simulated Blood Capillaries
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Reinforcement LearningSimilar Papers 제목 키워드 기반
Minute-Scale Training for Microrobot Navigation
Microrobots hold significant potential for various applications, where targeted navigation is a basic requirement. Deep reinforcement learning (DRL) has recently emerged as a powerful paradigm for fully autonomous micror…
Reinforcement LearningProgrammable Control of Ultrasound Swarmbots through Reinforcement Learning
Powered by acoustics, existing therapeutic and diagnostic procedures will become less invasive and new methods will become available that have never been available before. Acoustically driven microrobot navigation based …
DiagnosticNavigatereinforcement-learningReinforcement Learning+1Microrobot Vascular Parkour: Analytic Geometry-based Path Planning with Real-time Dynamic Obstacle Avoidance
Autonomous microrobots in blood vessels could enable minimally invasive therapies, but navigation is challenged by dense, moving obstacles. We propose a real-time path planning framework that couples an analytic geometry…
Reinforcement LearningSmart Magnetic Microrobots Learn to Swim with Deep Reinforcement Learning
Swimming microrobots are increasingly developed with complex materials and dynamic shapes and are expected to operate in complex environments in which the system dynamics are difficult to model and positional control of …
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Contrast-Free Autonomous Navigation of Untethered Endovascular Microrobots Using Single-Plane Fluoroscopy
Reliable three-dimensional (3D) navigation of magnetically actuated untethered microrobots remains a major barrier to clinical translation. X-ray fluoroscopy is the standard real-time imaging modality for endovascular pr…