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As medical ultrasound is becoming a prevailing examination approach nowadays,robotic ultrasound systems can facilitate the scanning process and preventprofessional sonographers from repetitive and tedious work. Despite the recentprogress, it is still a challenge to enable robots to autonomously accomplishthe ultrasound examination, which is largely due to the lack of a proper taskrepresentation method, and also an adaptation approach to generalize learnedskills across different patients. To solve these problems, we propose thelatent task representation and the robotic skills adaptation for autonomousultrasound in this paper. During the offline stage, the multimodal ultrasoundskills are merged and encapsulated into a low-dimensional probability modelthrough a fully self-supervised framework, which takes clinically demonstratedultrasound images, probe orientations, and contact forces into account. Duringthe online stage, the probability model will select and evaluate the optimalprediction. For unstable singularities, the adaptive optimizer fine-tunes themto near and stable predictions in high-confidence regions. Experimental resultsshow that the proposed approach can generate complex ultrasound strategies fordiverse populations and achieve significantly better quantitative results thanour previous method.

Learning Autonomous Ultrasound via Latent Task Representation and Robotic Skills Adaptation