AHCI RESEARCH GROUP

Publications

Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2025

Shi, L.; Gu, Y.; Zheng, Y.; Kameda, S.; Lu, H.

LWD-IUM: A Lightweight Detector for Advancing Robotic Grasp in VR-Based Industrial and Underwater Metaverse Proceedings Article

In: pp. 1384–1391, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331508876 (ISBN).

Abstract | Links | BibTeX | Tags: 3D object, 3D object detection, Deep learning, generative artificial intelligence, Grasping and manipulation, Intelligent robots, Learning systems, Metaverses, Neural Networks, Object Detection, Object recognition, Objects detection, Real- time, Real-time, Robotic grasping, robotic grasping and manipulation, Robotic manipulation, Virtual Reality, Vision transformer, Visual servoing

@inproceedings{shi_lwd-ium_2025,

title = {LWD-IUM: A Lightweight Detector for Advancing Robotic Grasp in VR-Based Industrial and Underwater Metaverse},

author = {L. Shi and Y. Gu and Y. Zheng and S. Kameda and H. Lu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011354353&doi=10.1109%2FIWCMC65282.2025.11059637&partnerID=40&md5=77aa4cdb0a08a1db5d0027a71403da89},

doi = {10.1109/IWCMC65282.2025.11059637},

isbn = {9798331508876 (ISBN)},

year  = {2025},

date = {2025-01-01},

pages = {1384–1391},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In the burgeoning field of virtual reality (VR) metaverse, the sophistication of interactions between robotic agents and their environment has become a critical concern. In this work, we present LWD-IUM, a novel light-weight detector designed to enhance robotic grasp capabilities in the VR metaverse. LWD-IUM applies deep learning techniques to discern and navigate the complex VR metaverse environment, aiding robotic agents in the identification and grasping of objects with high precision and efficiency. The algorithm is constructed with an advanced lightweight neural network structure based on self-attention mechanism that ensures optimal balance between computational cost and performance, making it highly suitable for real-time applications in VR. Evaluation on the KITTI 3D dataset demonstrated real-time detection capabilities (24-30 fps) of LWD-IUM, with its mean average precision (mAP) remaining 80% above standard 3D detectors, even with a 50% parameter reduction. In addition, we show that LWD-IUM outperforms existing models for object detection and grasping tasks through the real environment testing on a Baxter dual-arm collaborative robot. By pioneering advancements in robotic grasp in the VR metaverse, LWD-IUM promotes more immersive and realistic interactions, pushing the boundaries of what's possible in virtual experiences. © 2025 Elsevier B.V., All rights reserved.},

keywords = {3D object, 3D object detection, Deep learning, generative artificial intelligence, Grasping and manipulation, Intelligent robots, Learning systems, Metaverses, Neural Networks, Object Detection, Object recognition, Objects detection, Real- time, Real-time, Robotic grasping, robotic grasping and manipulation, Robotic manipulation, Virtual Reality, Vision transformer, Visual servoing},

pubstate = {published},

tppubtype = {inproceedings}

}

2024

Klein, A.; Arnowitz, E.

AI in mixed reality - Copilot on HoloLens: Spatial computing with large language models Proceedings Article

In: Spencer, S. N. (Ed.): Proc. - SIGGRAPH Real-Time Live!, Association for Computing Machinery, Inc, 2024, ISBN: 9798400705267 (ISBN).

Abstract | Links | BibTeX | Tags: 3D, AI, AR, Gesture, Gestures, HoloLens, Language Model, LLM, Mixed reality, Real- time, Real-time, Spatial computing, User experience design, User interfaces, Voice