Hugging Face Trending Papers
SonoWorld: Towards Interactive Pose-Controllable Ultrasound World Models
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arXiv Computer Vision
Oct 15
SonoWorld: Towards Interactive Pose-Controllable Ultrasound World Models
arXiv:2610.19327v1 Announce Type: new Abstract: Robotic ultrasound can standardize image acquisition and reduce operator workload and variability, but policy learning remains limited by the lack of r...
arXiv Computer Vision
Oct 8
UltraWorld: Learning Interactive Ultrasound World Models from Untracked Clinical Videos with Acoustic Sampling Map
arXiv:2610.09785v1 Announce Type: new Abstract: World models can enable autonomous ultrasound scanning by predicting the outcomes of probe motions from local observations. Learning this action--obser...
Hugging Face Trending Papers
Oct 7
UltraWorld: Learning Interactive Ultrasound World Models from Untracked Clinical Videos with Acoustic Sampling Map
World models can enable autonomous ultrasound scanning by predicting the outcomes of probe motions from local observations. Learning this action--observation relationship typically relies on synchroni...
arXiv AI
Aug 12
Surgical WAM: A World-Action Model for Data-Efficient Surgical Robot Learning
arXiv:2608. 11204v1 Announce Type: cross Abstract: Learning reliable surgical manipulation policies is bottlenecked by the scarcity of action-labeled demonstrations: teleoperated surgical robot (e.
Hugging Face Trending Papers
Oct 20
EndoWorld-JEPA: Motion-Guided Global-Local Latent World Modeling for Endoscopic Video Representation Learning
Endoscopic video contains state transitions induced by camera displacement, deformable tissue, and instrument interaction, yet existing pre-training methods treat dynamics implicitly. Inspired by acti...
arXiv AI
Aug 28
CLAP: Cross-Embodiment Video World Models are Zero-Shot Physical Simulators
CLAP is a cross-embodiment framework for action‑conditioned video generation that can be trained on diverse internet‑scale videos from both humans and robots. It reconciles different action spaces—end‑effector poses, language instructions, and latent actions—using a curriculum that first learns physics priors from unlabeled video and then grounds them in real‑world action spaces for zero‑shot deployment. The resulting models match or exceed state‑of‑the‑art single‑embodiment models in challenging environments and support few‑shot adaptation across a wide range of robot morphologies.