AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Dong, W.; Li, S.; Zheng, P.; Liu, L.; Chen, S.
A 3DGS and LLM-based physical-to-virtual approach for human-robot interactive manufacturing Journal Article
In: Manufacturing Letters, vol. 44, pp. 121–128, 2025, ISSN: 22138463 (ISSN), (Publisher: Elsevier Ltd).
Abstract | Links | BibTeX | Tags: 3D modeling, Gaussian distribution, Gaussians, High level languages, Human computer interaction, Human Robot Interaction, Human robots, Humans-robot interactions, Industrial robots, Language Model, Large language model, Man machine systems, Metaverses, Model-based OPC, Natural language processing systems, Physical-to-virtual, Robot programming, Robotic assembly, Splatting, Three dimensional computer graphics, Three-dimensional gaussian splatting
@article{dong_3dgs_2025,
title = {A 3DGS and LLM-based physical-to-virtual approach for human-robot interactive manufacturing},
author = {W. Dong and S. Li and P. Zheng and L. Liu and S. Chen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105014947667&doi=10.1016%2Fj.mfglet.2025.06.016&partnerID=40&md5=8fd8b07c1f2c71e46b396d2e244bf701},
doi = {10.1016/j.mfglet.2025.06.016},
issn = {22138463 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Manufacturing Letters},
volume = {44},
pages = {121–128},
abstract = {With the exploration of digital transformation in the industry, the introduction of the industrial metaverse is bringing unprecedented opportunities and challenges to the manufacturing industry. In the industrial metaverse, humans can interact safely and naturally with robots in high-fidelity digital environments, enabling non-technical operators to quickly validate industrial scenarios and help optimize decision-making and production processes. However, the complexity of Three-Dimensional (3D) modeling poses a challenge to achieving this goal. Additionally, programming-based Human Robot Interaction (HRI) also presents obstacles, as operators need significant time to learn how to control robots. Therefore, this paper proposes a 3D Gaussian Splatting (3DGS) and Large Language Model (LLM)-based physical-to-virtual approach for human-robot interactive manufacturing, which further facilitates digital interaction for non-technical operators in manufacturing environments. Specifically, 3DGS is first used for rapid visualization and reconstruction of the overall scene, achieving new perspective rendering and providing a gaussian ellipsoid representation. Then mesh extraction algorithms based on gaussian representation are used to build a physical-to-virtual transfer framework. Finally, LLM is utilized for understanding natural language commands and generating virtual robot Python programming to complete robot assembly tasks. This framework is implemented in the Isaac Sim simulator, and the case study shows that the proposed framework can quickly and accurately complete physical-to-virtual transfer and accomplish robot assembly manufacturing tasks in the simulator with low code. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Elsevier Ltd},
keywords = {3D modeling, Gaussian distribution, Gaussians, High level languages, Human computer interaction, Human Robot Interaction, Human robots, Humans-robot interactions, Industrial robots, Language Model, Large language model, Man machine systems, Metaverses, Model-based OPC, Natural language processing systems, Physical-to-virtual, Robot programming, Robotic assembly, Splatting, Three dimensional computer graphics, Three-dimensional gaussian splatting},
pubstate = {published},
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With the exploration of digital transformation in the industry, the introduction of the industrial metaverse is bringing unprecedented opportunities and challenges to the manufacturing industry. In the industrial metaverse, humans can interact safely and naturally with robots in high-fidelity digital environments, enabling non-technical operators to quickly validate industrial scenarios and help optimize decision-making and production processes. However, the complexity of Three-Dimensional (3D) modeling poses a challenge to achieving this goal. Additionally, programming-based Human Robot Interaction (HRI) also presents obstacles, as operators need significant time to learn how to control robots. Therefore, this paper proposes a 3D Gaussian Splatting (3DGS) and Large Language Model (LLM)-based physical-to-virtual approach for human-robot interactive manufacturing, which further facilitates digital interaction for non-technical operators in manufacturing environments. Specifically, 3DGS is first used for rapid visualization and reconstruction of the overall scene, achieving new perspective rendering and providing a gaussian ellipsoid representation. Then mesh extraction algorithms based on gaussian representation are used to build a physical-to-virtual transfer framework. Finally, LLM is utilized for understanding natural language commands and generating virtual robot Python programming to complete robot assembly tasks. This framework is implemented in the Isaac Sim simulator, and the case study shows that the proposed framework can quickly and accurately complete physical-to-virtual transfer and accomplish robot assembly manufacturing tasks in the simulator with low code. © 2025 Elsevier B.V., All rights reserved.
2024
Gkournelos, C.; Konstantinou, C.; Angelakis, P.; Michalos, G.; Makris, S.
Enabling Seamless Human-Robot Collaboration in Manufacturing Using LLMs Proceedings Article
In: A., Wagner; K., Alexopoulos; S., Makris (Ed.): Lect. Notes Mech. Eng., pp. 81–89, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 21954356 (ISSN); 978-303157495-5 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Collaboration capabilities, Computational Linguistics, Human operator, Human-Robot Collaboration, Industrial research, Industrial robots, Intelligent robots, Language Model, Large language model, large language models, Manufacturing environments, Programming robots, Reality interface, Research papers, Robot programming, User friendly
@inproceedings{gkournelos_enabling_2024,
title = {Enabling Seamless Human-Robot Collaboration in Manufacturing Using LLMs},
author = {C. Gkournelos and C. Konstantinou and P. Angelakis and G. Michalos and S. Makris},
editor = {Wagner A. and Alexopoulos K. and Makris S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199196139&doi=10.1007%2f978-3-031-57496-2_9&partnerID=40&md5=cd0b33b3c9e9f9e53f1e99882945e134},
doi = {10.1007/978-3-031-57496-2_9},
isbn = {21954356 (ISSN); 978-303157495-5 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Lect. Notes Mech. Eng.},
pages = {81–89},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In the era of Industry 5.0, there is a growing interest in harnessing the potential of human-robot collaboration (HRC) in manufacturing environments. This research paper focuses on the integration of Large Language Models (LLMs) to augment HRC capabilities, particularly in addressing configuration issues when programming robots to collaborate with human operators. By harnessing the capabilities of LLMs in combination with a user-friendly augmented reality (AR) interface, the proposed approach empowers human operators to seamlessly collaborate with robots, facilitating smooth and efficient assembly processes. This research introduces the CollabAI an AI assistant for task management and natural communication based on a fine-tuned GPT model focusing on collaborative manufacturing. Real-world experiments conducted in two manufacturing settings coming from the automotive and machinery industries. The findings have implications for various industries seeking to increase productivity and foster a new era of efficient and effective collaboration in manufacturing environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Artificial intelligence, Augmented Reality, Collaboration capabilities, Computational Linguistics, Human operator, Human-Robot Collaboration, Industrial research, Industrial robots, Intelligent robots, Language Model, Large language model, large language models, Manufacturing environments, Programming robots, Reality interface, Research papers, Robot programming, User friendly},
pubstate = {published},
tppubtype = {inproceedings}
}
In the era of Industry 5.0, there is a growing interest in harnessing the potential of human-robot collaboration (HRC) in manufacturing environments. This research paper focuses on the integration of Large Language Models (LLMs) to augment HRC capabilities, particularly in addressing configuration issues when programming robots to collaborate with human operators. By harnessing the capabilities of LLMs in combination with a user-friendly augmented reality (AR) interface, the proposed approach empowers human operators to seamlessly collaborate with robots, facilitating smooth and efficient assembly processes. This research introduces the CollabAI an AI assistant for task management and natural communication based on a fine-tuned GPT model focusing on collaborative manufacturing. Real-world experiments conducted in two manufacturing settings coming from the automotive and machinery industries. The findings have implications for various industries seeking to increase productivity and foster a new era of efficient and effective collaboration in manufacturing environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.