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
Song, T.; Pabst, F.; Eck, U.; Navab, N.
Enhancing Patient Acceptance of Robotic Ultrasound through Conversational Virtual Agent and Immersive Visualizations Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, vol. 31, no. 5, pp. 2901–2911, 2025, ISSN: 10772626 (ISSN).
Abstract | Links | BibTeX | Tags: 3D reconstruction, adult, Augmented Reality, Computer graphics, computer interface, echography, female, human, Humans, Imaging, Intelligent robots, Intelligent virtual agents, Language Model, male, Medical robotics, Middle Aged, Mixed reality, Patient Acceptance of Health Care, patient attitude, Patient comfort, procedures, Real-world, Reality visualization, Robotic Ultrasound, Robotics, Three-Dimensional, three-dimensional imaging, Trust and Acceptance, Ultrasonic applications, Ultrasonic equipment, Ultrasonography, Ultrasound probes, User-Computer Interface, Virtual agent, Virtual assistants, Virtual environments, Virtual Reality, Visual languages, Visualization, Young Adult
@article{song_enhancing_2025,
title = {Enhancing Patient Acceptance of Robotic Ultrasound through Conversational Virtual Agent and Immersive Visualizations},
author = {T. Song and F. Pabst and U. Eck and N. Navab},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003687673&doi=10.1109%2fTVCG.2025.3549181&partnerID=40&md5=1d46569933582ecf5e967f0794aafc07},
doi = {10.1109/TVCG.2025.3549181},
issn = {10772626 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Visualization and Computer Graphics},
volume = {31},
number = {5},
pages = {2901–2911},
abstract = {Robotic ultrasound systems have the potential to improve medical diagnostics, but patient acceptance remains a key challenge. To address this, we propose a novel system that combines an AI-based virtual agent, powered by a large language model (LLM), with three mixed reality visualizations aimed at enhancing patient comfort and trust. The LLM enables the virtual assistant to engage in natural, conversational dialogue with patients, answering questions in any format and offering real-time reassurance, creating a more intelligent and reliable interaction. The virtual assistant is animated as controlling the ultrasound probe, giving the impression that the robot is guided by the assistant. The first visualization employs augmented reality (AR), allowing patients to see the real world and the robot with the virtual avatar superimposed. The second visualization is an augmented virtuality (AV) environment, where the real-world body part being scanned is visible, while a 3D Gaussian Splatting reconstruction of the room, excluding the robot, forms the virtual environment. The third is a fully immersive virtual reality (VR) experience, featuring the same 3D reconstruction but entirely virtual, where the patient sees a virtual representation of their body being scanned in a robot-free environment. In this case, the virtual ultrasound probe, mirrors the movement of the probe controlled by the robot, creating a synchronized experience as it touches and moves over the patient's virtual body. We conducted a comprehensive agent-guided robotic ultrasound study with all participants, comparing these visualizations against a standard robotic ultrasound procedure. Results showed significant improvements in patient trust, acceptance, and comfort. Based on these findings, we offer insights into designing future mixed reality visualizations and virtual agents to further enhance patient comfort and acceptance in autonomous medical procedures. © 1995-2012 IEEE.},
keywords = {3D reconstruction, adult, Augmented Reality, Computer graphics, computer interface, echography, female, human, Humans, Imaging, Intelligent robots, Intelligent virtual agents, Language Model, male, Medical robotics, Middle Aged, Mixed reality, Patient Acceptance of Health Care, patient attitude, Patient comfort, procedures, Real-world, Reality visualization, Robotic Ultrasound, Robotics, Three-Dimensional, three-dimensional imaging, Trust and Acceptance, Ultrasonic applications, Ultrasonic equipment, Ultrasonography, Ultrasound probes, User-Computer Interface, Virtual agent, Virtual assistants, Virtual environments, Virtual Reality, Visual languages, Visualization, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Robotic ultrasound systems have the potential to improve medical diagnostics, but patient acceptance remains a key challenge. To address this, we propose a novel system that combines an AI-based virtual agent, powered by a large language model (LLM), with three mixed reality visualizations aimed at enhancing patient comfort and trust. The LLM enables the virtual assistant to engage in natural, conversational dialogue with patients, answering questions in any format and offering real-time reassurance, creating a more intelligent and reliable interaction. The virtual assistant is animated as controlling the ultrasound probe, giving the impression that the robot is guided by the assistant. The first visualization employs augmented reality (AR), allowing patients to see the real world and the robot with the virtual avatar superimposed. The second visualization is an augmented virtuality (AV) environment, where the real-world body part being scanned is visible, while a 3D Gaussian Splatting reconstruction of the room, excluding the robot, forms the virtual environment. The third is a fully immersive virtual reality (VR) experience, featuring the same 3D reconstruction but entirely virtual, where the patient sees a virtual representation of their body being scanned in a robot-free environment. In this case, the virtual ultrasound probe, mirrors the movement of the probe controlled by the robot, creating a synchronized experience as it touches and moves over the patient's virtual body. We conducted a comprehensive agent-guided robotic ultrasound study with all participants, comparing these visualizations against a standard robotic ultrasound procedure. Results showed significant improvements in patient trust, acceptance, and comfort. Based on these findings, we offer insights into designing future mixed reality visualizations and virtual agents to further enhance patient comfort and acceptance in autonomous medical procedures. © 1995-2012 IEEE.
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.
Peretti, A.; Mazzola, M.; Capra, L.; Piazzola, M.; Carlevaro, C.
Seamless Human-Robot Interaction Through a Distributed Zero-Trust Architecture and Advanced User Interfaces Proceedings Article
In: C., Secchi; L., Marconi (Ed.): Springer. Proc. Adv. Robot., pp. 92–95, Springer Nature, 2024, ISBN: 25111256 (ISSN); 978-303176427-1 (ISBN).
Abstract | Links | BibTeX | Tags: Advanced user interfaces, Digital Twins, HRC, Human Robot Interaction, Human-Robot Collaboration, Humans-robot interactions, Industrial robots, Industry 4.0, Intelligent robots, Interaction platform, Language Model, Large language model, LLM, Problem oriented languages, Robot Operating System, Robot operating system 2, Robot-robot collaboration, ROS2, RRC, Wages, XR, ZTA
@inproceedings{peretti_seamless_2024,
title = {Seamless Human-Robot Interaction Through a Distributed Zero-Trust Architecture and Advanced User Interfaces},
author = {A. Peretti and M. Mazzola and L. Capra and M. Piazzola and C. Carlevaro},
editor = {Secchi C. and Marconi L.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216090556&doi=10.1007%2f978-3-031-76428-8_18&partnerID=40&md5=9f58281f8a8c034fb45fed610ce64bd2},
doi = {10.1007/978-3-031-76428-8_18},
isbn = {25111256 (ISSN); 978-303176427-1 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Springer. Proc. Adv. Robot.},
volume = {33 SPAR},
pages = {92–95},
publisher = {Springer Nature},
abstract = {The proposed work presents a novel interaction platform designed to address the shortage of skilled workers in the labor market, facilitating the seamless integration of robotics and advanced user interfaces such as eXtended Reality (XR) to optimize Human-Robot Collaboration (HRC) as well as Robot-Robot Collaboration (RRC) in an Industry 4.0 scenario. One of the most challenging situations is to optimize and simplify the collaborations of humans and robots to decrease or avoid system slowdowns, blocks, or dangerous situations for both users and robots. The advent of the LLMs (Large Language Model) have been breakthrough the whole IT environment because they perform well in different scenario from human text generation to autonomous systems management. Due to their malleability, LLMs have a primary role for Human-Robot collaboration processes. For this reason, the platform comprises three key technical components: a distributed zero-trust architecture, a virtual avatar, and digital twins of robots powered by the Robot Operating System 2 (ROS2) platform. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Advanced user interfaces, Digital Twins, HRC, Human Robot Interaction, Human-Robot Collaboration, Humans-robot interactions, Industrial robots, Industry 4.0, Intelligent robots, Interaction platform, Language Model, Large language model, LLM, Problem oriented languages, Robot Operating System, Robot operating system 2, Robot-robot collaboration, ROS2, RRC, Wages, XR, ZTA},
pubstate = {published},
tppubtype = {inproceedings}
}
The proposed work presents a novel interaction platform designed to address the shortage of skilled workers in the labor market, facilitating the seamless integration of robotics and advanced user interfaces such as eXtended Reality (XR) to optimize Human-Robot Collaboration (HRC) as well as Robot-Robot Collaboration (RRC) in an Industry 4.0 scenario. One of the most challenging situations is to optimize and simplify the collaborations of humans and robots to decrease or avoid system slowdowns, blocks, or dangerous situations for both users and robots. The advent of the LLMs (Large Language Model) have been breakthrough the whole IT environment because they perform well in different scenario from human text generation to autonomous systems management. Due to their malleability, LLMs have a primary role for Human-Robot collaboration processes. For this reason, the platform comprises three key technical components: a distributed zero-trust architecture, a virtual avatar, and digital twins of robots powered by the Robot Operating System 2 (ROS2) platform. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Sonawani, S.; Weigend, F.; Amor, H. B.
SiSCo: Signal Synthesis for Effective Human-Robot Communication Via Large Language Models Proceedings Article
In: IEEE Int Conf Intell Rob Syst, pp. 7107–7114, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 21530858 (ISSN); 979-835037770-5 (ISBN).
Abstract | Links | BibTeX | Tags: Communications channels, Extensive resources, Human engineering, Human Robot Interaction, Human-Robot Collaboration, Human-robot communication, Humans-robot interactions, Industrial robots, Intelligent robots, Language Model, Man machine systems, Microrobots, Robust communication, Signal synthesis, Specialized knowledge, Visual communication, Visual cues, Visual languages
@inproceedings{sonawani_sisco_2024,
title = {SiSCo: Signal Synthesis for Effective Human-Robot Communication Via Large Language Models},
author = {S. Sonawani and F. Weigend and H. B. Amor},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216466596&doi=10.1109%2fIROS58592.2024.10802561&partnerID=40&md5=ccd14b4f0b5d527b179394dffd4e2c73},
doi = {10.1109/IROS58592.2024.10802561},
isbn = {21530858 (ISSN); 979-835037770-5 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE Int Conf Intell Rob Syst},
pages = {7107–7114},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Effective human-robot collaboration hinges on robust communication channels, with visual signaling playing a pivotal role due to its intuitive appeal. Yet, the creation of visually intuitive cues often demands extensive resources and specialized knowledge. The emergence of Large Language Models (LLMs) offers promising avenues for enhancing human-robot interactions and revolutionizing the way we generate context-aware visual cues. To this end, we introduce SiSCo-a novel framework that combines the computational power of LLMs with mixed-reality technologies to streamline the creation of visual cues for human-robot collaboration. Our results show that SiSCo improves the efficiency of communication in human-robot teaming tasks, reducing task completion time by approximately 73% and increasing task success rates by 18% compared to baseline natural language signals. Additionally, SiSCo reduces cognitive load for participants by 46%, as measured by the NASA-TLX subscale, and receives above-average user ratings for on-the-fly signals generated for unseen objects. To encourage further development and broader community engagement, we provide full access to SiSCo's implementation and related materials on our GitHub repository.1 © 2024 IEEE.},
keywords = {Communications channels, Extensive resources, Human engineering, Human Robot Interaction, Human-Robot Collaboration, Human-robot communication, Humans-robot interactions, Industrial robots, Intelligent robots, Language Model, Man machine systems, Microrobots, Robust communication, Signal synthesis, Specialized knowledge, Visual communication, Visual cues, Visual languages},
pubstate = {published},
tppubtype = {inproceedings}
}
Effective human-robot collaboration hinges on robust communication channels, with visual signaling playing a pivotal role due to its intuitive appeal. Yet, the creation of visually intuitive cues often demands extensive resources and specialized knowledge. The emergence of Large Language Models (LLMs) offers promising avenues for enhancing human-robot interactions and revolutionizing the way we generate context-aware visual cues. To this end, we introduce SiSCo-a novel framework that combines the computational power of LLMs with mixed-reality technologies to streamline the creation of visual cues for human-robot collaboration. Our results show that SiSCo improves the efficiency of communication in human-robot teaming tasks, reducing task completion time by approximately 73% and increasing task success rates by 18% compared to baseline natural language signals. Additionally, SiSCo reduces cognitive load for participants by 46%, as measured by the NASA-TLX subscale, and receives above-average user ratings for on-the-fly signals generated for unseen objects. To encourage further development and broader community engagement, we provide full access to SiSCo's implementation and related materials on our GitHub repository.1 © 2024 IEEE.