  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.
