AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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Here you can find the complete list of our 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.
2024
Xi, M.; Perera, M.; Matthews, B.; Wang, R.; Weiley, V.; Somarathna, R.; Maqbool, H.; Chen, J.; Engelke, U.; Anderson, S.; Adcock, M.; Thomas, B. H.
Towards Immersive AI Proceedings Article
In: U., Eck; M., Sra; J., Stefanucci; M., Sugimoto; M., Tatzgern; I., Williams (Ed.): Proc. - IEEE Int. Symp. Mixed Augment. Real. Adjunct, ISMAR-Adjunct, pp. 260–264, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833150691-9 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Data visualization, Decision making, Heterogenous data, Immersive, Immersive analytic, Immersive analytics, Industrial research, Mixed reality, Neuro-symbolic system, Real- time, Scientific paradigm, Situated imaging., Time-interleaved, Visual analytics, Work-flows
@inproceedings{xi_towards_2024,
title = {Towards Immersive AI},
author = {M. Xi and M. Perera and B. Matthews and R. Wang and V. Weiley and R. Somarathna and H. Maqbool and J. Chen and U. Engelke and S. Anderson and M. Adcock and B. H. Thomas},
editor = {Eck U. and Sra M. and Stefanucci J. and Sugimoto M. and Tatzgern M. and Williams I.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214375967&doi=10.1109%2fISMAR-Adjunct64951.2024.00062&partnerID=40&md5=fd07c97119d71418bb4365582b1d188c},
doi = {10.1109/ISMAR-Adjunct64951.2024.00062},
isbn = {979-833150691-9 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Symp. Mixed Augment. Real. Adjunct, ISMAR-Adjunct},
pages = {260–264},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {With every shift in scientific paradigms comes not only a new way of seeing the world, but as Kunh argues, new tools for seeing [13]. Today, generative AI and neuro-symbolic systems show signs of changing how science functions, making it possible to synthesise complex heterogenous data in real time, interleaved with complex and situated workflows. But the new tools are not yet fully formed. To realise the opportunities and meet the challenges posed by the growth of generative AI for science and other knowledge work requires us to look beyond improvements in algorithms. The decision-making landscape for information workers has drastically changed, and the pressing need for analysts and experts to collaborate with AI in complex, high-tempo data environments has never been more evident.To bring strategic focus to these challenges in ways that will enable social, environmental and economic benefits for all, CSIRO's Data61 (the data and digital specialist arm of the Commonwealth Scientific and Industrial Research Organisation - Australia's national science agency) has established the Immersive AI Research Cluster. The cluster allows more than 30 research scientists and engineers to focus on defining a broad range of scientific disciplines for people to work with and understand the information provided by AI, such as data visualisation, visual analytics, connecting remote people, through immersive technologies like virtual and augmented reality. This workshop paper presents the trending research directions and challenges that emerged from this research cluster, which are closely linked to the scientific domains and illustrated through use cases. © 2024 IEEE.},
keywords = {Artificial intelligence, Augmented Reality, Data visualization, Decision making, Heterogenous data, Immersive, Immersive analytic, Immersive analytics, Industrial research, Mixed reality, Neuro-symbolic system, Real- time, Scientific paradigm, Situated imaging., Time-interleaved, Visual analytics, Work-flows},
pubstate = {published},
tppubtype = {inproceedings}
}
With every shift in scientific paradigms comes not only a new way of seeing the world, but as Kunh argues, new tools for seeing [13]. Today, generative AI and neuro-symbolic systems show signs of changing how science functions, making it possible to synthesise complex heterogenous data in real time, interleaved with complex and situated workflows. But the new tools are not yet fully formed. To realise the opportunities and meet the challenges posed by the growth of generative AI for science and other knowledge work requires us to look beyond improvements in algorithms. The decision-making landscape for information workers has drastically changed, and the pressing need for analysts and experts to collaborate with AI in complex, high-tempo data environments has never been more evident.To bring strategic focus to these challenges in ways that will enable social, environmental and economic benefits for all, CSIRO's Data61 (the data and digital specialist arm of the Commonwealth Scientific and Industrial Research Organisation - Australia's national science agency) has established the Immersive AI Research Cluster. The cluster allows more than 30 research scientists and engineers to focus on defining a broad range of scientific disciplines for people to work with and understand the information provided by AI, such as data visualisation, visual analytics, connecting remote people, through immersive technologies like virtual and augmented reality. This workshop paper presents the trending research directions and challenges that emerged from this research cluster, which are closely linked to the scientific domains and illustrated through use cases. © 2024 IEEE.
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.