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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 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, Attentive Support is capable to decide when and how to s...
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Manuel Dietrich and Jörg Pohle , "Robot Design for Social Intervenability", NordiCHI 2024, 2024.
AbstractAdvanced assistive technologies like robots must go beyond being merely service-on-request devices but should be equipped with abilities to coexist in social environments. Many development activities concentrate on refining robot capabilities for understanding complex social nuances. In this paper, we argue for shifting focus; rather than aiming for flawless operation through optimized context understanding, strong emphasis should be put into des...
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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, whic...
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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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Felix Lanfermann, Thiago de Jesus de Araujo Rios, Stefan Menzel , "Large Language Model-assisted Clustering and Concept Identification of Engineering Design Data", IEEE Conference on Artificial Intelligence, 2024.
AbstractRecent advances in Large Language Models (LLM) open up opportunities for users to interact with domain spe- cific knowledge and execute (semi-)professional tasks in a dia- log fashion. Without profound knowledge in data science and programming languages, basic statistics and further detailed analyses can be conducted intuitively through natural language prompts. Accessing common data science methods, LLMs can assist users in visualizing, i...
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Svenja Kenneweg, Philipp Cimiano, Jörg Deigmöller, Julian Eggert , "Benchmarking the Ability of Large Language Models to Reason about Event Sequences", Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KEOD), 2024.
AbstractThe ability to reason about events and their temporal relations is a key aspect in Natural Language Understanding. In this paper, we investigate the ability of Large Language Models to resolve temporal references with respect to longer event sequences. Given that events rarely occur in isolation, it is crucial to determine the extent to which Large Language Models can reason about longer sequences of events. Towards this goal, we introduce a nov...
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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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Judith Sieker, Nazia Attari, Heiko Wersing, Simeon Schüz, Hendrik Buschmeier, Sina Zarriess , "The Illusion of Competence: Evaluating the Effect of Explanations On Users' Mental Models of Visual Question Answering Systems", The 2024 Conference on Empirical Methods in Natural Language Processing, 2024.
AbstractIn our study, we examine how participants/users perceive the limitations of an AI system when it encounters tasks it cannot perform perfectly. Our objective is to investigate whether providing explanations alongside model answers aids users in building an appropriate mental model of an AI's limitations. To accomplish this, we employ a visual question explanation task and evaluate both the accuracy of the models' answers and the effectiveness of t...
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Angus Kenny, Tapabrata Ray, Steffen Limmer, Hemant Kumar 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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