论文标题
超声引导的机器人导航，并进行深入增强学习
Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning
论文作者
论文摘要
在本文中，我们介绍了第一个基于强化的机器人导航方法，该方法利用超声（US）图像作为输入。我们的方法结合了最新的RL技术，特别是深层Q-Networks（DQN）和内存缓冲区和二进制分类器，用于决定何时终止任务。 我们的方法对34名志愿者的内部收集数据集进行了培训和评估，与纯RL和监督学习（SL）技术相比，它的性能要好得多，这突出了RL导航对美国提供程序的适用性。在测试我们提出的模型时，我们获得了82.91％的机会，可以从5个不同看不见的模拟环境上从165个不同的起始位置正确导航到ac骨。
In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input. Our approach combines state-of-the-art RL techniques, specifically deep Q-networks (DQN) with memory buffers and a binary classifier for deciding when to terminate the task. Our method is trained and evaluated on an in-house collected data-set of 34 volunteers and when compared to pure RL and supervised learning (SL) techniques, it performs substantially better, which highlights the suitability of RL navigation for US-guided procedures. When testing our proposed model, we obtained a 82.91% chance of navigating correctly to the sacrum from 165 different starting positions on 5 different unseen simulated environments.
