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Eleftherios Triantafyllidis
About me
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Posts
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portfolio
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publications
Study of Multimodal Interfaces and the Improvements on Teleoperation
Published in IEEE Access, 2020
This paper examines how different sensory combinations entailing a full factorial comparison of auditory, visual and somatosensory feedback states, influence motor task performance in 3D space. Through a series of increasingly more complex motor tasks with human subjects, a correlation is established between multimodal interfaces and their effects on motor performance.
Recommended citation: Eleftherios Triantafyllidis, Christopher Mcgreavy, Jiacheng Gu and Zhibin Li, "Study of Multimodal Interfaces and the Improvements on Teleoperation," in IEEE Access, vol. 8, pp. 78213-78227, 2020, doi: 10.1109/ACCESS.2020.2990080. https://arxiv.org/pdf/2003.14392.pdf
Considerations and Challenges of Measuring Operator Performance in Telepresence and Teleoperation Entailing Mixed Reality Technologies
Published in ACM CHI 2021 (Evaluating User Experiences in Mixed Reality Workshop), 2021
This paper presents an overview of existing methods in assessing human motor performance and considerations for improving presence in telepresence and teleoperation entailing mixed reality technologies. We also outline potential future challenges and frontiers for deriving a standardised set of guidelines for the derivation of a unified immersion framework.
Recommended citation: Eleftherios Triantafyllidis and Zhibin Li. 2021. Considerations and Challenges of Measuring Operator Performance in Telepresence and Teleoperation Entailing Mixed Reality Technologies. In CHI Conference on Human Factors in Computing Systems Workshop CHI 2021 (Evaluating User Experiences in Mixed Reality). Association for Computing Machinery, May 7, 2021, Yokohama, Japan. ACM, New York, NY, USA. https://arxiv.org/pdf/2103.12702.pdf
The Challenges in Modeling Human Performance in 3D Space with Fitts Law
Published in ACM CHI 2021, 2021
This paper presents and analyses the most widely used human motor performance metric extensions based on Fitts law. The aim is to aid the derivation of a unified and standardised metric for measuring and quantifying motor task performance; commonly seen in robotic teleoperation and VR-based interactions in full 3D space entailing varying degrees of freedom.
Recommended citation: Eleftherios Triantafyllidis and Zhibin Li. 2021. The Challenges in Modeling Human Performance in 3D Space with Fitts Law. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (CHI EA 2021). Association for Computing Machinery, New York, NY, USA, Article 56, 1–9. https://doi.org/10.1145/3411763.3443442 https://arxiv.org/pdf/2101.00260.pdf
Metrics for 3D Object Pointing and Manipulation in Virtual Reality: The Introduction and Validation of a Novel Approach in Measuring Human Performance
Published in IEEE Robotics & Automation Magazine (IEEE RAM), 2022
This paper introduces a new performance metric based on Fitts law, designed to model human motor performance across full 3D space with varying degrees of freedom. This metric serves as a distinctive tool to quantify prevalent VR-based interactions and robotic teleoperation movements, increasing inter-study comparability in these domains.
Recommended citation: Eleftherios Triantafyllidis, Wenbin Hu, Christopher McGreavy and Zhibin Li, "Metrics for 3D Object Pointing and Manipulation in Virtual Reality: The Introduction and Validation of a Novel Approach in Measuring Human Performance," in IEEE Robotics & Automation Magazine, vol. 29, no. 1, pp. 76-91, March 2022, doi: 10.1109/MRA.2021.3090070. https://arxiv.org/pdf/2106.06655.pdf
Modular Neural Network Policies for Learning In-Flight Object Catching with a Robot Hand-Arm Systems
Published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023
The paper introduces a modular framework for training robot hand-arm systems to catch flying objects, combining supervised learning for trajectory prediction and pose ranking with deep reinforcement learning for reaching and grasping actions. Extensive simulations show high success rates with the system generalising beyond its training to catch various household objects.
Recommended citation: Wenbin Hu, Fernando Acero, Eleftherios Triantafyllidis, Zhaocheng Liu and Zhibin Li. (2023). "Modular Neural Network Policies for Learning In-flight Object Catching with a Robot Hand-Arm System." in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://ieeexplore.ieee.org/abstract/document/10341463
Identifying Important Sensory Feedback for Learning Locomotion Skills
Published in Nature Machine Intelligence, 2023
This paper presents a saliency analysis to determine the most crucial feedback states for motor skills in deep reinforcement learning, showing that using only key states, a simulated robot can perform various locomotion tasks robustly. The suggested approach can be applied to differentiable state-action mappings, like those in neural network control strategies, allowing for the acquisition of various motor skills with fewer sensing requirements.
Recommended citation: Wanming Yu, Chuanyu Yang, Christopher McGreavy, Eleftherios Triantafyllidis, Guillaume Bellegarda, Milad Shafiee, Auke Jan Ijspeert and Zhibin Li (2023). "Identifying Important Sensory Feedback for Learning Locomotion Skills." in Nature Machine Intelligence (NMI) 2023. https://www.nature.com/articles/s42256-023-00701-w
Hybrid Hierarchical Learning for Solving Complex Sequential Tasks Using the RObotic MAnipulation Network - ROMAN
Published in Nature Machine Intelligence, 2023
This paper presents a novel framework known as the Robotic Manipulation Network (ROMAN). ROMAN is biological-inspired Hybrid Hierarchical Learning (HHL) architecture to address the challenges of complex long-horizon sequential robotic manipulation tasks that furthermore entail sparse rewards.
Recommended citation: Eleftherios Triantafyllidis, Fernando Acero, Zhaocheng Liu, Zhibin Li (2023). "RObotic MAnipulation Network (ROMAN) - Hybrid Hierarchical Learning for Solving Complex Sequential Tasks." in Nature Machine Intelligence (NMI) 2023. https://www.nature.com/articles/s42256-023-00709-2
Intrinsic Language-Guided Exploration for Complex Long-Horizon Robotic Manipulation Tasks
Published in International Conference on Robotics and Automation (ICRA), 2024, 2024
We present a new approach called Intrinsically Guided Exploration from Large Language Models (IGE-LLMs) to enhance reinforcement learning in complex, feedback-sparse environments. IGE-LLMs utilize language models to guide exploration effectively.
Recommended citation: Eleftherios Triantafyllidis, Filippos Christianos, Zhibin Li (2024). "Intrinsic Language-Guided Exploration for Complex Long-Horizon Robotic Manipulation Tasks" in International Conference on Robotics and Automation (ICRA), May 2024, Yokohama, Japan. https://arxiv.org/abs/2309.16347
Advancements in Sensory-Motor Perception and Biologically-Inspired Hierarchical Learning for Embodied Intelligence
Published in Doctoral Thesis, 2024
Recommended citation: Eleftherios Triantafyllidis (2024). "Advancements in Sensory-Motor Perception and Biologically-Inspired Hierarchical Learning for Embodied Intelligence". Doctoral Dissertation. The University of Edinburgh, February, 2024. Edinburgh, United Kingdom. https://era.ed.ac.uk/handle/1842/41453
talks
Journal Presentation - IEEE Access
Published:
A presentation of my journal paper Study of Multimodal Interfaces and the Improvements on Teleoperation publised in the IEEE Access journal and can be accessed on YouTube. The paper is also available as open access and be downloaded here.
Conference Workshop Talk - ACM CHI 2021
Published:
I presented my paper Considerations and Challenges of Measuring Operator Performance in Telepresence and Teleoperation Entailing Mixed Reality Technologies at the Workshop “Evaluating User Experiences in Mixed Reality” at the leading Human-Computer Interaction conference ACM Conference on Human Factors in Computing Systems (CHI). My presentation was recorded and can be accessed on YouTube. The workshop program can be found here.
Conference Proceeding Talk - ACM CHI 2021
Published:
I presented my paper The Challenges in Modeling Human Performance in 3D Space with Fitts’ Law at the leading Human-Computer Interaction conference ACM Conference on Human Factors in Computing Systems (CHI). My presentation was recorded and can be accessed on YouTube. The conference program can be found here.
Journal Presentation - IEEE Robotics and Automation Magazine (RAM)
Published:
A presentation of my journal paper Metrics for 3D Object Pointing and Manipulation in Virtual Reality: The Introduction and Validation of a Novel Approach in Measuring Human Performance publised in the IEEE Robotics and Automation Magazine can be accessed on YouTube. The paper is also available to be downloaded as a preprint.
Journal Presentation - Nature Machine-Intelligence
Published:
The journal paper Hybrid Hierarchical Learning for Solving Complex Sequential Tasks Using the RObotic MAnipulation Network - ROMAN can be accessed in Nature Machine Intelligence, with a video presentation of the work also available on YouTube.
Early Access - arXiv Pre-Print
Published:
Our paper Intrinsic Language-Guided Exploration for Complex Long-Horizon Robotic Manipulation Tasks can be accessed as an early access pre-print on arXiv, with a video presentation of the work also available on YouTube.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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