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Tobias Rodemann and Christiane Attig, "How can digital twins help to accelerate the transition to a carbon-neutral energy system", EuroCAST (Computer Aided System Theory), 2024.
AbstractGood investment decisions into green energy systems are hard due to high budget requests, complex systems and multiple objectives to consider. Simulation models of current and potential future energy systems (so called digital twins) can help to generate a deeper understanding, better recommendations, and more reliable forecasts of costs and savings. We developed detailed simulation models for our R&D facility (see Fig. 1) using a simulation appr...
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Laurenz Tomandl, Thomas Jatschka, Guenther Raidl, Tobias Rodemann, "A Learning Bilevel Optimization Approach for the Demand Maximizing Battery Swapping Station Location Problem", Eurocast, 2024 19th International Conference on Computer Aided Systems Theory, 2024.
AbstractA problem for the wide-scale adoption of electric vehicles are the usually long battery charging times. To avoid the waiting time for the customer, vehicles with exchangeable batteries and a network of battery swapping stations are a promising solution for smaller-scale vehicles like electric scooters. A customer can drive to a station and exchange their depleted batteries with an already charged battery and thus avoid the waiting that would be n...
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Daniel Tanneberg, Felix Ocker, Stephan Hasler, Jörg Deigmöller, Anna Belardinelli, Chao Wang, Heiko Wersing, Bernhard Sendhoff, Michael Gienger, "To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions", Arxiv, no. arXiv:2403.12533, 2024.
AbstractHow can a robot support a group of humans in physical group activities? We present \textit{Attentive Support}, a novel interaction concept for robots to support a group of humans. It combines scene perception, dialogue acquisition, situation understanding, and behavior generation with the common-sense reasoning capabilities of Large Language Models (LLMs). In addition to following user instructions, \textit{Attentive Support} is capable to decide...
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Lei Yan, Theodoros Stouraitis, Joao Moura, Michael Gienger, Sethu Vijayakumar, "Impact-Aware Bimanual Catching of Large-Momentum Objects", IEEE Transaction on Robotics, 2024.
AbstractThis paper investigates one of the most challeng- ing tasks in dynamic manipulation—catching large-momentum moving objects. Beyond the realm of quasi-static manipulation, dealing with highly dynamic objects can significantly improve the robot’s capability of interacting with its surrounding en- vironment. Yet, the inevitable motion mismatch between the fast moving object and the approaching robot will result in large impulsive forces, ...
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Felix Lanfermann, Qiqi Liu, Yaochu Jin, Sebastian Schmitt, "Identification of Energy Management Configuration Concepts from a Set of Pareto-optimal Solutions", Energy Conversion and Management: X, vol. 22, pp. 100576, 2024.
AbstractOptimizing building configurations for an efficient use of energy is increasingly receiving attention by current research and several methods have been developed to address this task. Selecting a suitable configuration based on multiple conflicting objectives, such as initial investment cost, recurring cost, robustness with respect to uncertainty of grid operation is, however, a difficult multi-criteria decision making problem. Concept identifica...
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Frank Joublin and Antonello Ceravola, "Exploration of Generative model at Honda-Research Institute", Meetup at SRH University Heidelberg, 2024.
AbstractIn this talk we present at the Generative AI conference the HRI-EU institute at first, then we recap the evolution of AI in the trends of LLM and their applicability in different domains and products. We touch on the main exposed limitation of LLM and a sample of the different solution the community and the different AI companies came to. We then pick 3 investigated use-cases HRI-EU did on the usage of generative AI: Text to 3D generation in car ...
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Angus Kenny, Tapabrata Ray, Steffen Limmer, Hemant Singh, Tobias Rodemann, Markus Olhofer, "A Hierarchical Dissimilarity Metric for Automated Machine Learning Pipelines, and Visualizing Search Behaviour", Evostar 2024, 2024.
AbstractIn this study, the challenge of developing a dissimilarity metric for machine learning pipeline optimization is addressed. Traditional approaches, limited by simplified operator sets and pipeline structures, fail to address the full complexity of this task. Two novel metrics are proposed for measuring structural, and hyperparameter, dissimilarity in the decision space. A hierarchical approach is employed to integrate these metrics, prioritizing s...
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Johannes Varga, Guenther Raidl, Tobias Rodemann, "Selecting User Queries in Interactive Job Scheduling", Eurocast 2024, 19th International Conference on Computer Aided Systems Theory, 2024.
AbstractWe consider a class of job scheduling problems in which human users, e.g., the personnel of a company, need to perform jobs on some shared machines and the availabilities of these users as well as the machines is critical. In such situations it is rarely practical to ask users to fully specify their availability times. Instead we assume users initially only propose a single starting time for each of their jobs, and a feasible and optimized schedu...
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Johannes Varga, Guenther Raidl, Elina Rönnberg, Tobias Rodemann, "Scheduling Jobs Involving Humans with few Interaction Rounds for Learning Human Availabilities", Computers & Operations Research, vol. 167, pp. 106648, 2024.
AbstractThe solution to a job scheduling problem that involves humans as well some other shared resource has to consider the humans’ availability times. For practical acceptance of a scheduling tool, it is crucial that the interaction with the humans is kept simple and to a minimum. It is rarely practical to ask users to fully specify their availability times or to let them enumerate all possible starting times for their jobs. In the scenario we are c...
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Mitra Baratchi, Can Wang, Steffen Limmer, Jan van Rijn, Holger Hoos, Thomas Bäck, Markus Olhofer, "Automated Machine Learning: Past, Present and Future", Artificial Intelligence Review, 2024.
AbstractAutomated Machine Learning (AutoML) is a young research area aiming at making high-performance machine learning techniques accessible to a broad set of users. This is achieved by identifying all design choices in creating a machine-learning model and addressing them automatically to generate performance-optimised models. In this article, we provide an extensive overview of the past and present, as well as future perspectives of AutoML. First, we ...
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