Sim4EndoR：血管内ロボティクスのタスク自動化に向けた強化学習ベースのシミュレーションプラットフォーム
Sim4EndoR: A Reinforcement Learning Centered Simulation Platform for Task Automation of Endovascular Robotics
経皮的冠動脈インターベンション（PCI）のタスク自律化に向け、血管の解剖学的制約を報酬関数に組み込んだ強化学習ベースのシミュレーション環境を提案し、sim-to-real転移を促進する。
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著者: Tianliang Yao, Madaoji Ban, Bo Lu, Zhiqiang Pei, Peng Qi
分類: cs.RO
原文アブストラクト
Robotic-assisted percutaneous coronary intervention (PCI) holds considerable promise for elevating precision and safety in cardiovascular procedures. Nevertheless, current systems heavily depend on human operators, resulting in variability and the potential for human error. To tackle these challenges, Sim4EndoR, an innovative reinforcement learning (RL) based simulation environment, is first introduced to bolster task-level autonomy in PCI. This platform offers a comprehensive and risk-free environment for the development, evaluation, and refinement of potential autonomous systems, enhancing data collection efficiency and minimizing the need for costly hardware trials. A notable aspect of the groundbreaking Sim4EndoR is its reward function, which takes into account the anatomical constraints of the vascular environment, utilizing the geometric characteristics of vessels to steer the learning process. By seamlessly integrating advanced physical simulations with neural network-driven policy learning, Sim4EndoR fosters efficient sim-to-real translation, paving the way for safer, more consistent robotic interventions in clinical practice, ultimately improving patient outcomes.
