An RL-fine-tuned small model lets a robot follow ultrasound guidelines to scan gallbladder, spine, and kidney autonomously
The work proposes an autonomous robotic ultrasound framework driven by an LLM agent that retrieves guideline steps from scanning handbooks, reasons over current observations and scanning state, and dynamically invokes tools for trajectory planning, robot execution, contact adjustment, voice guidance, and trajectory refinement, with reinforcement-learning (PPO) fine-tuning to improve reasoning quality and the correctness of tool selection and parameterization; validated verbally on 10 unseen ultrasound scanning guidelines, fine-tuning raised step-wise accuracy from 0.6512 to 0.8973 and overall success rate from 0.5384 to 0.