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Tim Puphal, Jens Schmüdderich, Nils Einecke , "Risk Estimation for Advanced Driver Assistance Systems", Biped Launch Event, 2022.
AbstractWe are invited by biped to hold a short talk at their launch event in Lausanne on 15.12. In the talk we are going to present the risk estimation developed at HRI-EU as well as a short introduction to the HRI. ...
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Christoph Bergmeir, Frits de Nijs, Abishek Sriramulu, Mahdi Abolghasemi, Richard Bean, John Betts, Quang Bui, Nam Trong Dinh, Nils Einecke, Rasul Esmaeilbeigi, Scott Ferraro, Priya Galketiya, Evgenii Genov, Robert Glasgow, Rakshitha Godahewa, Yanfei Kang, Steffen Limmer, Luis Magdalena, Pablo Montero-Manso, Daniel Peralta-Camara, Yogesh Pipada Sunil Kumar, Alejandro Rosales-Perez, Julian Ruddick, Akylas Stratigakos, Peter Stuckey, Guido Tack, Isaac Triguero, Rui Yuan , "Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy", arxiv, 2022.
AbstractAlgorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chain (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be...
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Fabio Muratore, Fabio Ramos, Wenhao Yu, Greg Turk, Michael Gienger, Jan Peters , "Robot Learning from Randomized Simulations: A Review", Frontiers Robotics and AI, 2022.
AbstractThe rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require giant amounts of data. It is prohibitively expensive to generate such data sets on a physical platform. Therefore, state-of-the art approaches learn in simulation where data generation is fast as well as inexpensive, and subsequently transfer the knowledge to the real robot sim-to-real. Despite becoming more and more realistic, all simulators...
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Julian Eggert and Johane Takeuchi , "Graph Based Pattern Classification for NLU and Slot Filling: Approach and Analysis", NLPIR '22: Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval, pp. 65–70, 2022.
AbstractIn Natural Language Understanding, semantic parsing refers to the task of extracting meaningful words from text, with slot filling being a special case. At the same time, syntactic parsing deals with the identification of syntactic structure in the text. In this paper, we analyze to which extent syntactic structure can be used for semantic parsing. For this purpose, we represent the syntactic structure of sentences from an annotated databas...
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Qiqi Liu, Ran Cheng, Yaochu Jin, Martin Heiderich, Tobias Rodemann , "Reference Vector Assisted Adaptive Model Management for Surrogate-Assisted Many-objective Optimization", IEEE Transactions on Systems, Man and Cybernetics: Systems, 2022.
AbstractAcquisition functions for surrogate-assisted many-objective optimization require a delicate balance between convergence and diversity. To meet this requirement, we propose an adaptive model management strategy assisted by two sets of reference vectors, one set of adaptive reference vectors accounting for convergence while the other set of fixed reference vectors for diversity. Specifically, we first propose a new acquisition function that calcu...
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Muhammad Haris, Mathias Franzius, Ute Bauer-Wersing , "Physical Interactive Localization Learning ", 2022 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO), 2022.
AbstractLocalization is fundamental for mobile robots, especially in unconstrained outdoor environments. Earlier work showed unsupervised localization learning on landmarks to be suitable for large-scale scenes. However, this relied on hand-labeled data to train a CNN for recognizing landmarks. We propose a new approach that allows a robot to learn landmarks for localization with a human cooperatively. This approach uses pre-trained detectors of c...
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Matih Ullah , "Landmark Independent Visual Localization with Slow Feature Analysis", University of Applied Sciences Frankfurt , 2022.
AbstractVisual localization is an area of interest to research in mobile robots, self-driving cars, etc. Localization using a camera is one of the fundamental requirements for a vision-based mobile robot. In this context, unsupervised learning with Slow Feature Analysis (SFA) directly applies to the images to extract a spatial representation of the environment. In the past, SFA is used with Convolutional Neural Network (CNN) to...
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Meike Elisabeth Kühne, Tim Schrills, Markus Gödker, Patricia Wollstadt, Thomas Franke , "Subjective Information Processing Awareness for Intelligent Charging Agents - Connecting Traceability, Trust & Users’ Ability to Predict", DGPS Kongress 2022, Deutsche Gesellschaft fuer Psychologie, 2022.
AbstractWhile interacting with artificial intelligence (AI), users experience automated information processing, which can remain untraceable to them. This involves evaluating options in the area of intelligent bidirectional charging of electric vehicles (EV). Untraceable information processing can have negative effects on the cooperation between humans and AI, since it will not be recognizable to humans according to which reference values specific chargi...
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Nivesh Dommaraju, Mariusz Bujny, Stefan Menzel, Markus Olhofer, Fabian Duddeck , "Evaluation of geometric similarity metrics for structural clusters generated using topology optimization", Applied Intelligence, 2022.
AbstractIn an engineering design process, multitudes of feasible designs can be automatically generated using structural optimization methods by varying the design requirements or user preferences for different performance objectives. Design exploration of such potentially large datasets is a challenging task. An unsupervised data-centric approach for exploring designs is to find clusters of similar designs and recommend only the cluster representat...
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Sneha Saha , "Learning-based Generative Representations for Automotive Design Optimization", University of Birmingham, 2022.
AbstractIn this thesis, we envisioned a cooperative design system (CDS) which learn from the existing 3D designs generated during past optimization cycles and is able to generate potential alternatives to assist designer's ideation process. The research in this thesis, address different aspects that can be combined to form a CDS framework. First, based on the survey of deep learning techniques, a point cloud variational autoencoder is adapted from the li...
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