arXiv subjects

Jingsong Mu

Publications and source records attributed to Jingsong Mu.

2 recordsLinked to original sources

arXiv2025

AIA-UltraNeRF:Acoustic-Impedance-Aware Neural Radiance Field with Hash Encodings for Robotic Ultrasound Reconstruction and Localization

Neural radiance field (NeRF) is a promising approach for reconstruction and new view synthesis. However, previous NeRF-based reconstruction methods overlook the critical role of acoustic impedance in ultrasound imaging. Localization methods face challenges related to local minima due to the selection of initial poses. In this study, we design a robotic ultrasound system (RUSS) with an acoustic-impedance-aware ultrasound NeRF (AIA-UltraNeRF) to decouple the scanning and diagnostic processes. Specifically, AIA-UltraNeRF models a continuous function of hash-encoded spatial coordinates for the 3D ultrasound map, allowing for the storage of acoustic impedance without dense sampling. This approach accelerates both reconstruction and inference speeds. We then propose a dual-supervised network that leverages teacher and student models to hash-encode the rendered ultrasound images from the reconstructed map. AIA-UltraNeRF retrieves the most similar hash values without the need to render images again, providing an offline initial image position for localization. Moreover, we develop a RUSS with a spherical remote center of motion mechanism to hold the probe, implementing operator-independent scanning modes that separate image acquisition from diagnostic workflows. Experimental results on a phantom and human subjects demonstrate the effectiveness of acoustic impedance in implicitly characterizing the color of ultrasound images. AIAUltraNeRF achieves both reconstruction and localization with inference speeds that are 9.9 faster than those of vanilla NeRF.

cs.RO↗

arXiv2025

A Semantic-Aware Framework for Safe and Intent-Integrative Assistance in Upper-Limb Exoskeletons

Upper-limb exoskeletons are primarily designed to provide assistive support by accurately interpreting and responding to human intentions. In home-care scenarios, exoskeletons are expected to adapt their assistive configurations based on the semantic information of the task, adjusting appropriately in accordance with the nature of the object being manipulated. However, existing solutions often lack the ability to understand task semantics or collaboratively plan actions with the user, limiting their generalizability. To address this challenge, this paper introduces a semantic-aware framework that integrates large language models into the task planning framework, enabling the delivery of safe and intent-integrative assistance. The proposed approach begins with the exoskeleton operating in transparent mode to capture the wearer's intent during object grasping. Once semantic information is extracted from the task description, the system automatically configures appropriate assistive parameters. In addition, a diffusion-based anomaly detector is used to continuously monitor the state of human-robot interaction and trigger real-time replanning in response to detected anomalies. During task execution, online trajectory refinement and impedance control are used to ensure safety and regulate human-robot interaction. Experimental results demonstrate that the proposed method effectively aligns with the wearer's cognition, adapts to semantically varying tasks, and responds reliably to anomalies.

cs.RO↗