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 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},
tppubtype = {article}
}
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
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.
Fujii, A.; Fukuda, K.
Initial Study on Robot Emotional Expression Using Manpu Proceedings Article
In: ACM/IEEE Int. Conf. Hum.-Rob. Interact., pp. 463–467, IEEE Computer Society, 2024, ISBN: 21672148 (ISSN); 979-840070323-2 (ISBN).
Abstract | Links | BibTeX | Tags: Comic engineering, Comic symbol, Comic symbols, Display devices, Emotional expressions, Express emotions, Generic expression, Human Robot Interaction, Human robots, Human-robot interaction, Humans-robot interactions, Machine design, Man machine systems, Manpu, Mixed reality, Symbiotics, Symbolic methods
@inproceedings{fujii_initial_2024,
title = {Initial Study on Robot Emotional Expression Using Manpu},
author = {A. Fujii and K. Fukuda},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188120223&doi=10.1145%2f3610978.3640652&partnerID=40&md5=4277cbd98c0474e2e7ba19d352e6f46e},
doi = {10.1145/3610978.3640652},
isbn = {21672148 (ISSN); 979-840070323-2 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {ACM/IEEE Int. Conf. Hum.-Rob. Interact.},
pages = {463–467},
publisher = {IEEE Computer Society},
abstract = {In recent years, robots have started to play an active role in various places in society. The ability of robots not only to convey information but also to interact emotionally, is necessary to realize a human-robot symbiotic society. Many studies have been conducted on the emotional expression of robots. However, as robots come in a wide variety of designs, it is difficult to construct a generic expression method, and some robots are not equipped with expression devices such as faces or displays. To address these problems, this research aims to develop technology that enables robots to express emotions, using Manpu (a symbolic method used in comic books, expressing not only the emotions of humans and animals but also the states of objects) and mixed reality technology. As the first step of the research, we categorize manpu and use large language models to generate manpu expressions according to the dialogue information. © 2024 Copyright held by the owner/author(s)},
keywords = {Comic engineering, Comic symbol, Comic symbols, Display devices, Emotional expressions, Express emotions, Generic expression, Human Robot Interaction, Human robots, Human-robot interaction, Humans-robot interactions, Machine design, Man machine systems, Manpu, Mixed reality, Symbiotics, Symbolic methods},
pubstate = {published},
tppubtype = {inproceedings}
}
In recent years, robots have started to play an active role in various places in society. The ability of robots not only to convey information but also to interact emotionally, is necessary to realize a human-robot symbiotic society. Many studies have been conducted on the emotional expression of robots. However, as robots come in a wide variety of designs, it is difficult to construct a generic expression method, and some robots are not equipped with expression devices such as faces or displays. To address these problems, this research aims to develop technology that enables robots to express emotions, using Manpu (a symbolic method used in comic books, expressing not only the emotions of humans and animals but also the states of objects) and mixed reality technology. As the first step of the research, we categorize manpu and use large language models to generate manpu expressions according to the dialogue information. © 2024 Copyright held by the owner/author(s)
2023
Bottega, J. A.; Kich, V. A.; Jesus, J. C.; Steinmetz, R.; Kolling, A. H.; Grando, R. B. Bedin; Guerra, R. S. Silva; Gamarra, D. F. T. Tello
Jubileo: An Immersive Simulation Framework for Social Robot Design Journal Article
In: Journal of Intelligent and Robotic Systems: Theory and Applications, vol. 109, no. 4, 2023, ISSN: 09210296 (ISSN); 15730409 (ISSN), (Publisher: Springer Nature).
Abstract | Links | BibTeX | Tags: Anthropomorphic Robots, Computational Linguistics, Cost effectiveness, E-Learning, English language learning, English languages, Human Robot Interaction, Human-robot interaction, Humanoid robot, Humans-robot interactions, Immersive, Language learning, Language Model, Large language model, large language models, Learning game, Machine design, Man machine systems, Open systems, Robot Operating System, Simulation framework, Simulation platform, Virtual Reality
@article{bottega_jubileo_2023,
title = {Jubileo: An Immersive Simulation Framework for Social Robot Design},
author = {J. A. Bottega and V. A. Kich and J. C. Jesus and R. Steinmetz and A. H. Kolling and R. B. Bedin Grando and R. S. Silva Guerra and D. F. T. Tello Gamarra},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178895874&doi=10.1007%2Fs10846-023-01991-3&partnerID=40&md5=667b3b88a61ee8a62969484157edc9cd},
doi = {10.1007/s10846-023-01991-3},
issn = {09210296 (ISSN); 15730409 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {Journal of Intelligent and Robotic Systems: Theory and Applications},
volume = {109},
number = {4},
abstract = {This paper introduces Jubileo, an open-source simulated humanoid robot as a framework for the development of human-robot interaction applications. By leveraging the power of the Robot Operating System (ROS) and Unity in a virtual reality environment, this simulation establishes a strong connection to real robotics, faithfully replicating the robot’s physical components down to its motors and enabling communication with servo-actuators to control both the animatronic face and the joints of a real humanoid robot. To validate the capabilities of the framework, we propose English teaching games that integrate Virtual Reality (VR), game-based Human-Robot Interaction (HRI), and advanced large language models such as Generative Pre-trained Transformer (GPT). These games aim to foster linguistic competence within dynamic and interactive virtual environments. The incorporation of large language models bolsters the robot’s capability to generate human-like responses, thus facilitating a more realistic conversational experience. Moreover, the simulation framework reduces real-world testing risks and offers a cost-effective, efficient, and scalable platform for developing new HRI applications. The paper underscores the transformative potential of converging VR, large language models, and HRI, particularly in educational applications. © 2024 Elsevier B.V., All rights reserved.},
note = {Publisher: Springer Nature},
keywords = {Anthropomorphic Robots, Computational Linguistics, Cost effectiveness, E-Learning, English language learning, English languages, Human Robot Interaction, Human-robot interaction, Humanoid robot, Humans-robot interactions, Immersive, Language learning, Language Model, Large language model, large language models, Learning game, Machine design, Man machine systems, Open systems, Robot Operating System, Simulation framework, Simulation platform, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
This paper introduces Jubileo, an open-source simulated humanoid robot as a framework for the development of human-robot interaction applications. By leveraging the power of the Robot Operating System (ROS) and Unity in a virtual reality environment, this simulation establishes a strong connection to real robotics, faithfully replicating the robot’s physical components down to its motors and enabling communication with servo-actuators to control both the animatronic face and the joints of a real humanoid robot. To validate the capabilities of the framework, we propose English teaching games that integrate Virtual Reality (VR), game-based Human-Robot Interaction (HRI), and advanced large language models such as Generative Pre-trained Transformer (GPT). These games aim to foster linguistic competence within dynamic and interactive virtual environments. The incorporation of large language models bolsters the robot’s capability to generate human-like responses, thus facilitating a more realistic conversational experience. Moreover, the simulation framework reduces real-world testing risks and offers a cost-effective, efficient, and scalable platform for developing new HRI applications. The paper underscores the transformative potential of converging VR, large language models, and HRI, particularly in educational applications. © 2024 Elsevier B.V., All rights reserved.