Mobile manipulators enable a wide range of operations with mobility and advanced manipulation capabilities. Despite their potential, existing approaches typically treat the mobile base and the manipulator separately, thereby limiting the optimality of the system for composite whole-body behaviors. In this work, we present a Whole-Body Model Predictive Control framework for mobile manipulation involving tasks with varying timelines. We integrate task priorities across both task and time dimensions, bringing inherent transition ability with enhanced performance. Our approach improves the trajectory tracking performance by up to 36 % in terms of manipulability and reduces the maximum velocity during task priority transitions by 53% compared to the existing approach while maintaining a low computational cost of 4.3 ms, allowing for high reactivity in real-world applications. We demonstrate its effectiveness through a door-opening and traversing behavior, showcasing the first successful implementation of a non-holonomic mobile manipulator in such a scenario. See https://wbmpc.github.io/ for supplemental materials.

Ultrasound scanning is a critical imaging technique for real-time, non-invasive diagnostics. However, variations in patient anatomy and complex human-in-the-loop interactions pose significant challenges for autonomous robotic scanning. Existing ultrasound scanning robots are commonly limited to relatively low generalization and inefficient data utilization. To overcome these limitations, we present UltraDP, a Diffusion-Policy-based method that receives multi-sensory inputs (ultrasound images, wrist camera images, contact wrench, and probe pose) and generates actions that are fit for multi-modal action distributions in autonomous ultrasound scanning of carotid artery. We propose a specialized guidance module to enable the policy to output actions that center the artery in ultrasound images. To ensure stable contact and safe interaction between the robot and the human subject, a hybrid force-impedance controller is utilized to drive the robot to track such trajectories. Also, we have built a large-scale training dataset for carotid scanning comprising 210 scans with 460k sample pairs from 21 volunteers of both genders. By exploring our guidance module and DP’s strong generalization ability, UltraDP achieves a 95% success rate in transverse scanning on previously unseen subjects, demonstrating its effectiveness.