# Learning Autonomous Ultrasound via Latent Task Representation and Robotic Skills Adaptation

arXiv: https://arxiv.org/abs/2307.13323 · HTML: https://mlsanity.com/paper/2307.13323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives](https://mlsanity.com/paper/2307.05545/) (2023)
- [Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound](https://mlsanity.com/paper/2007.04480/) (2020)
- [Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.00718/) (2021)
- [DopUS-Net: Quality-Aware Robotic Ultrasound Imaging based on Doppler Signal](https://mlsanity.com/paper/2305.08938/) (2023)
- [Robotic Sonographer: Autonomous Robotic Ultrasound using Domain Expertise in Bayesian Optimization](https://mlsanity.com/paper/2307.02442/) (2023)
- [Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and Guided Explorations](https://mlsanity.com/paper/2111.01625/) (2021)
- [Learning Ultrasound Scanning Skills from Human Demonstrations](https://mlsanity.com/paper/2111.09739/) (2021)

## Abstract (arXiv)

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.