通过人类示范和引导探索学习机器人超声扫描技能
Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and Guided Explorations
AI总结：
本文提出一种基于人类示范和引导探索的学习方法，将超声扫描技能建模为多模态模型，利用模仿学习训练并后优化，以实现机器人自主超声扫描。
摘要：
医学超声已成为当今常规检查手段，并被广泛应用于不同的医疗应用，因此期望拥有能够自主执行超声扫描的机器人超声系统。然而，超声扫描技能相当复杂，高度依赖于超声医师的经验。在本文中，我们提出一种基于学习的方法，从人类示范中学习机器人超声扫描技能。首先，将机器人超声扫描技能封装为高维多模态模型，该模型综合考虑超声图像、探头位姿/位置和接触力。其次，我们利用模仿学习的力量，使用从经验丰富的超声医师示范中收集的训练数据来训练多模态模型。最后，提出一种带引导探索的后优化程序，以进一步提高所学模型的性能。我们进行了机器人实验，以验证所提出框架和所学模型的优势。
英文摘要：
Medical ultrasound has become a routine examination approach nowadays and is widely adopted for different medical applications, so it is desired to have a robotic ultrasound system to perform the ultrasound scanning autonomously. However, the ultrasound scanning skill is considerably complex, which highly depends on the experience of the ultrasound physician. In this paper, we propose a learning-based approach to learn the robotic ultrasound scanning skills from human demonstrations. First, the robotic ultrasound scanning skill is encapsulated into a high-dimensional multi-modal model, which takes the ultrasound images, the pose/position of the probe and the contact force into account. Second, we leverage the power of imitation learning to train the multi-modal model with the training data collected from the demonstrations of experienced ultrasound physicians. Finally, a post-optimization procedure with guided explorations is proposed to further improve the performance of the learned model. Robotic experiments are conducted to validate the advantages of our proposed framework and the learned models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
