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Andreas Sochopoulos, Michael Gienger, Sethu Vijayakumar , "Learning Deep Dynamical Systems via Stable Neural ODEs", IEEE International Conference on Intelligent Robots and Systems (IROS), 2024.
AbstractLearning robust and intricate trajectories from demonstrations in robotic tasks has been effectively addressed through the utilization of Dynamical Systems (DS). State-of- the-art DS learning methods ensure stability of the generated trajectories however they have three shortcomings: a) the Ds is assumed to have a single attractor, b) state derivative infor- mation is assumed to be available in the learning process and c) the state of the ...
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Felix Ocker and Julian Eggert , "Accessing Knowledge using Retrieval Augmented Generation", Honda Technical Forum 2023, 2023.
AbstractLanguage Models (LMs) provide an intuitive interface for humans via Natural Language. However, they hallucinate very convincingly and do not have access to proprietary data. This presentation gives insights into Retrieval Augmented Generation (RAG) as a technology for realizing the LM experience for large amounts of proprietary data. We present the underlying architecture, results achieved with an HRI-EU internal prototype for the TikiWiki, the "...
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Patricia Wollstadt, Sebastian Schmitt, Michael Wibral , "A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition", Journal of Machine Learning Research, vol. 24, no. 131, pp. 1-44, 2023.
AbstractSelecting a minimal feature set that is maximally informative about a target variable is a central task in machine learning and statistics. Information theory provides a powerful framework for formulating feature selection algorithms---yet, a rigorous, information-theoretic definition of feature relevancy, which accounts for feature interactions such as redundant and synergistic contributions, is still missing. We argue that this lack is inherent...
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Andrea Castellani , "Dealing with Inaccurate and Incomplete Labels in Industrial Streaming Data - Talk at Uni Creete (27.09.2023)", Crete University, Crete University, 2023.
AbstractMachine learning techniques are an essential option for processing large volumes of data and are capable to capture complex relationships within it. However, obtaining meaningfully annotated data is a real challenge and typically incurs large costs. Especially, in an industrial setting where few labelled data samples are available and drifting data features poses a severe challenge. In this talk, I will address: (1) how to efficiently train model...
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Andrea Castellani , "Dealing with Inaccurate and Incomplete Labels in Industrial Streaming Data", Uni Bielefeld, Uni Bielefeld, 2023.
AbstractThe pressure to increase the energetic efficiency of industrial facilities has led to a strong increase in the number of installed measurement sensors. These collect large volumes of data that need to be processed and analyzed. As manual data processing methods are not appropriate due to the sheer amount of data, automated and intelligent solutions are needed. Machine learning techniques are a viable option for processing large volumes of da...
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Daniel Gordon, Andreas Christou, Michael Gienger, Sethu Vijayakumar , "Adaptive Assistive Robotics: A Framework For Triadic Collaboration Between Humans and Robots", Royal Society Open Science, 2023.
AbstractRobots and other assistive technologies have a huge potential to help society in domains ranging from factory work to healthcare. However, safe and eff ective control of robotic agents in these environments is complex, especially when it involves close interactions and multiple actors. We propose an eff ective framework for optimising the behaviour of robots and complementary assistive technologies in systems comprising a mix of human and...
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Jan Leusmann, Chao Wang, Michael Gienger, Albrecht Schmidt, Sven Mayer , "Understanding the Uncertainty Loop of Human-Robot Interaction", Workshop paper of CHI 23, 2023.
AbstractRecently the field of Human-Robot Interaction gained popularity, due to the wide range of possibilities of how robots can support humans during daily tasks. One form of supportive robots are socially assistive robots which are specifically built for communicating with humans, e.g., as service robots or personal companions. As they understand humans through artificial intelligence, these robots will at some point make wrong assumptions about the h...
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Noah Wach , " Data re-uploading with a single qudit", Quantum Research Seminars Toronto, 2023.
AbstractInvited Talk at Quantum Research Seminar Toronto...
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Zhenpeng Shi, Nikolay Matyunin, Kalman György Graffi, David Starobinski , "Uncovering CWE-CVE-CPE Relations with Threat Knowledge Graphs", arXiv, 2023.
AbstractSecurity assessment relies on public information about products, vulnerabilities, and weaknesses. So far, databases in these categories have rarely been analyzed in combination. Yet, doing so could help predict unreported vulnerabilities and identify common threat patterns. In this paper, we propose a methodology for producing and optimizing a knowledge graph that aggregates knowledge from common threat databases (CVE, CWE, and CPE). We apply the...
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Anna Belardinelli , "Gaze-based intention estimation: principles, methodologies, and applications in HRI", arxiv, 2023.
AbstractIntention prediction has become a relevant field of research in Human-Machine and Human-Robot Interaction. Indeed, any artificial system (co)-operating with and along humans, designed to assist and coordinate its actions with a human partner, would benefit from first inferring the human’s current intention. To spare the user the cognitive burden of explicitly uttering their goals, this inference relies mostly on behavioral cues deemed indicati...
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