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The paper introduces Sim4EndoR, a reinforcement learning (RL) based simulation environment designed to enhance task-level autonomy in robotic-assisted percutaneous coronary intervention (PCI). Sim4EndoR leverages advanced physical simulations and neural network-driven policy learning within a risk-free environment to develop and evaluate autonomous systems for PCI. The platform's reward function incorporates anatomical constraints derived from vessel geometry, facilitating efficient sim-to-real transfer.
Sim4EndoR offers a risk-free environment for training autonomous endovascular robots, using vessel geometry in the reward function to improve sim-to-real transfer.
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.
