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arXiv Robotics — research abstracts· Keke Yang, Erqi Wang, Sainan Guan, Hongliang Ren·· 11 days agoEditorial score56

UltraWorld: Learning Interactive Ultrasound World Models from Untracked Clinical Videos with Acoustic Sampling Map

UltraWorld: Learning Interactive Ultrasound World Models from Untracked Clinical Videos with Acoustic Sampling Map

Summary

UltraWorld is a self-distillation approach that transfers priors from clinical ultrasound videos into interactive world models without real action annotations. It adapts a video foundation model into an ultrasound generator conditioned on reference images and anatomical masks, using programmable trajectories through 3D anatomy to synthesize action-video pairs. The Acoustic Sampling Map (AsMap) represents probe poses and imaging settings as pixel-wise 3D sampling positions, beam directions, and depths. Experiments show improved prediction fidelity and action following, with reductions in mean final distance to the goal and orientation error compared to visual servoing.

Source: arXiv Robotics — research abstracts · Read original article ↗

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SimulationOpen source S5
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and imaging settings as pixel-wise 3D sampling positions, beam directions, and depths. Experiments demonstrate improved prediction fidelity and action following. Across nine simulated closed-loop local planning episodes, UltraWorld reduces the mean final distance to the goal and orientation error by 29\% and 38\%, respectively, compared with visual servoing. Project Page: https://ultraworld-project.github.io/.

Open source S5

Source:arXiv Robotics — research abstracts · arxiv.org

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