Anatomy-Anchored Learning Framework for Robotic Ultrasound
Overview
This research introduces an anatomy-anchored imitation learning framework for robotic ultrasound scanning. The framework combines anatomy-guided probe placement with learned ultrasound navigation, enabling autonomous scanning with high task success rates. The approach reduces reliance on third-person vision and achieves 96%, 94%, and 88% success in kidney, gallbladder, and liver standard-plane acquisitions, respectively, with significant improvements over baseline models.
From arXiv Robotics — research abstracts
Event evidence and corrections
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“hand scanning without a robot. On 16 unseen participants, task success reaches 96%, 94%, 88%, and 94% for kidney, gallbladder, and liver standard-plane acquisition and radial artery sweeping, respectively. Compared with a fine-tuned pi_0 model, success on the abdominal tasks improves by 10-42 percentage points, with lower time-to-target, peak contact force, and trajectory jerk. Ablations support the complementary rol”
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“hand scanning without a robot. On 16 unseen participants, task success reaches 96%, 94%, 88%, and 94% for kidney, gallbladder, and liver standard-plane acquisition and radial artery sweeping, respectively. Compared with a fine-tuned pi_0 model, success on the abdominal tasks improves by 10-42 percentage points, with lower time-to-target, peak contact force, and trajectory jerk. Ablations support the complementary rol”
Exact source · revision 1Source owner not reported
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- 2026-10-30T20:07:52.191Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T20:07:46.567Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalFrom Freehand Demonstrations to Autonomous Robotic Ultrasound: An Anatomy-Anchored Learning Framework
This research introduces an anatomy-anchored imitation learning framework for robotic ultrasound scanning. The framework combines anatomy-guided probe placement with learned ultrasound navigation, enabling autonomous scanning with high task success rates. The approach reduces reliance on third-person vision and achieves 96%, 94%, and 88% success in kidney, gallbladder, and liver standard-plane acquisitions, respectively, with significant improvements over baseline models.
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