通过潜在任务表示和机器人技能自适应学习自主超声
Learning Autonomous Ultrasound via Latent Task Representation and Robotic Skills Adaptation
AI总结：
本文提出潜在任务表示与机器人技能自适应方法，通过自监督概率模型融合多模态超声技能，实现自主超声检查并适应不同患者，实验验证其性能显著优于先前方法。
摘要：
随着医学超声如今成为一种普遍采用的检查方法，机器人超声系统能够促进扫描过程，并使专业超声技师免于重复且繁琐的工作。尽管近期取得了进展，但让机器人自主完成超声检查仍是一个挑战，这主要归因于缺乏合适的任务表示方法，以及缺乏一种能够将所学技能推广到不同患者的自适应方法。为解决这些问题，本文提出了用于自主超声的潜在任务表示和机器人技能自适应方法。在离线阶段，通过一个完全自监督的框架，将多模态超声技能合并并封装到低维概率模型中，该框架考虑了临床演示的超声图像、探头方向和接触力。在在线阶段，概率模型将选择并评估最优预测。对于不稳定的奇异点，自适应优化器将其微调至高置信度区域中接近且稳定的预测。实验结果表明，所提出的方法能够为不同人群生成复杂的超声策略，并在定量结果上显著优于我们之前的方法。
英文摘要：
As medical ultrasound is becoming a prevailing examination approach nowadays, robotic ultrasound systems can facilitate the scanning process and prevent professional sonographers from repetitive and tedious work. Despite the recent progress, it is still a challenge to enable robots to autonomously accomplish the ultrasound examination, which is largely due to the lack of a proper task representation method, and also an adaptation approach to generalize learned skills across different patients. To solve these problems, we propose the latent task representation and the robotic skills adaptation for autonomous ultrasound in this paper. During the offline stage, the multimodal ultrasound skills are merged and encapsulated into a low-dimensional probability model through a fully self-supervised framework, which takes clinically demonstrated ultrasound images, probe orientations, and contact forces into account. During the online stage, the probability model will select and evaluate the optimal prediction. For unstable singularities, the adaptive optimizer fine-tunes them to near and stable predictions in high-confidence regions. Experimental results show that the proposed approach can generate complex ultrasound strategies for diverse populations and achieve significantly better quantitative results than our previous method.
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。
