CrossWorld: Learning Contact-Rich Robotic Ultrasound Policies from Complementary Partial Worlds

Learning robotic ultrasound policies typically relies on complete real-world expert demonstrations that jointly capture spatial probe motion, contact force, and ultrasound observations, making data collection costly and difficult to scale. Simulation offers extensive and efficient spatial exploration but lacks faithful contact-dependent dynamics that are essential for robotic ultrasound scanning. Conversely, such dynamics can be captured through local force-controlled real interactions without requiring complete expert demonstrations. Since neither domain alone provides the full experience required for policy learning, this raises a natural question: can two complementary, low-cost partial worlds be composed to reduce the need for costly complete real-world demonstrations? We present CrossWorld, a cross-domain world-modeling framework that learns from a real contact world and a simulated spatial world through cross-world factor completion. Specifically, contact completion transfers appearance dynamics learned from local real interactions to complement simulated observations, while spatial completion transfers action-conditioned spatial dynamics learned in simulation to complement sparse real observations. The policy then jointly perceives each factual B-mode observation and its cross-world completed view through a paired co-observation interface, enabling effective policy learning with a limited number of complete real-world expert demonstrations. On L4 standard-plane navigation, CrossWorld with OpenVLA achieves 70.0% cross-subject success in simulation, compared with 67.5% for simulation-only training. With local contact interactions, 50 phantom expert demonstrations, and a fixed set of 60 human demonstrations, CrossWorld achieves 75.0% success on phantom scanning, compared with 25.0% for direct mixing of simulated and expert demonstrations. These results demonstrate that composing complementary partial worlds can substantially reduce reliance on costly real-world expert demonstrations for robotic ultrasound policy learning.