Jointly with roboticists, Tianliang Yao, Yueqi Xu, Haoyu Wang, Xihe Qiu and Peng Qi, Professor Kaspar Althoefer has published a paper on 'Multi-Agent Fuzzy Reinforcement Learning with Large Language Models for Cooperative Navigation of Endovascular Robotics' in IEEE Transactions on Fuzzy Systems.

The paper summarises recent work on context-aware, autonomous navigation of guidewires and catheters in complex vascular systems. Leveraging fuzzy reinforcement learning (a more human-like logic allowing for vagueness or nuance), the group's approach ensures efficiency and precision, surpassing traditional methods in 3D simulators.

Endovascular interventions require precise, cooperative control of multiple instruments, such as guidewires and catheters, to navigate complex vascular anatomies. Current robotic systems, reliant on leader-follower control, depend heavily on operator expertise and lack intelligence. Learning-based methods, often limited to single-instrument control, fall short in complex clinical scenarios requiring multi-instrument coordination.

This study proposes a Multi-Agent Fuzzy Reinforcement Learning (MAFRL) framework, guided by large language models (LLMs), for task-level autonomous, cooperative navigation in endovascular robotics. LLMs provide procedural priors and context-aware policy guidance, enabling adaptive decision-making for collaborative guidewire and catheter agents. Central to the framework, fuzzy reinforcement learning mitigates LLM-induced uncertainties by adaptively embedding clinical constraints into reward functions, ensuring strict adherence to procedural safety and precise alignment with the complexities of real-world endovascular interventions.

Validated in a 3D vascular simulation, this approach achieves superior navigation performance and procedural efficiency compared to conventional methods, underscoring the transformative potential of fuzzy reinforcement learning in advancing LLM-guided MARL for endovascular robotics.