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Felix Ocker, Jörg Deigmöller, Julian Eggert , "Exploring Large Language Models as a Source of Common-Sense Knowledge for Robots", International Semantic Web Conference, 2023.
AbstractService robots need common-sense knowledge to help humans in everyday situations as it enables them to understand the context of their actions. However, approaches that use ontologies face a challenge because common-sense knowledge is often implicit, i.e., it is obvious to humans but not explicitly stated. This paper investigates if Large Language Models (LLMs) can fill this gap. Our experiments reveal limited effectiveness in the selective extra...
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Jonathan Jakob, Martina Hasenjäger, Barbara Hammer , "Incremental Human Gait Prediction without Catastrophic forgetting", IEEE SSCI 2023, 2023.
AbstractHuman gait prediction is an important task in predictive exoskeleton control. However, if static models are used to facilitate this task, two problems arise. First, the models cannot adapt to new environments and terrains during deployment, and second, the models cannot be personalized to any given end user without costly involvement of a human expert. Incremental models can alleviate these shortcomings, but they usually are prone to catastrophic...
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David Rother, Thomas H Weisswange, Jan Peters , "Summary: Disentangling Interaction using Maximum Entropy Reinforcement Learning in Multi-Agent Systems", AAAI 2023 Fall Symposia: Agent Teaming in Mixed-Motive Situations, 2023.
AbstractResearch on multi-agent interaction involving both artifi- cial agents and humans is still in its infancy. Current ap- proaches often focus on collaboration-centered human be- havior or a limited set of predefined situations, potentially limiting their efficacy in ”coexistence” environments. These are scenarios likely to arise in future deployments of robots in human-inhabited spaces, where interactions won’t always align with predefined m...
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Theodoros Stouraitis and Michael Gienger , "Predictive and Robust Robot Assistance for Sequential Manipulation Tasks", IEEE Research and Automation Letters (RA-L), 2023.
AbstractThis paper presents a novel concept to support impaired users in daily physical object manipulation tasks with a robot. Starting out with an assumed manipulation task of a user, we propose a predictive model that uniquely casts the user's sequential behavior as well as a robot support intervention into a hierarchical multi-objective optimization problem. A major contribution is the prediction formulation, which allows to model several different f...
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Thomas Jatschka, Matthias Rauscher, Bernhard Kreutzer, Yusuke Okamoto, Hiroaki Kataoka , Tobias Rodemann, Guenther Raidl , "A Large Neighborhood Search for Battery Swapping Station Location Planning for Electric Scooters", EuroCAST Conference 2022, pp. 121-129, 2023.
AbstractA major hindrance for the large-scale adoption of electric vehicles (EVs) are the long battery recharging times. An attractive alternative to directly recharging batteries of EVs is to replace them. While this is not easily possible for electric cars due to the weight and the size of the batteries, those used in electric scooters are small enough to be handled with ease. Hence, they can be exchanged directly by the customers at designated batter...
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Steffen Limmer and Nils Einecke , "SPOC 2023: Approaches by HRI", Space Optimization Competition Workshop, 2023.
AbstractPresent approach for 2023 ESA / GECCO optimization challenge at special SpOC workshop held by the ESA....
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Jiawen Kong , "Learning Class-Imbalanced Problems from the Perspective of Data Intrinsic Characteristics", Leiden University, 2023.
AbstractThe class-imbalance problem is a challenging classification task and is frequently encountered in real-world applications. Various techniques have been developed to improve the imbalanced classification performance theoretically and practically. Apart from developing new approaches, researchers also address the importance of understanding the data itself, which will provide more insight into what actually hinders the imbalanced classification per...
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Sibghat Ullah , "Model-assisted robust optimization for continuous black-box problems", Leiden University, 2023.
AbstractWhile solving real-world optimization problems, e.g., in the area of automotive engineering, building construction, and steel production, the issue of uncertainty and noise is frequently-encountered. Common sources of uncertainty and noise include search/decision variables (that describe the system to be optimized), the environmental variables or operating conditions the system is subject to, the evaluation of the (physical) system (or model of t...
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Margarita Veshchezerova, Mikhail Somov, David Bertsche, Steffen Limmer, Sebastian Schmitt, Michael Perelshtein , "A Hybrid Quantum-Classical Approach to the Electric Mobility Problem", IEEE Quantum Week 2023, 2023.
AbstractWe suggest a hybrid quantum-classical routine for the NP-hard Electric Vehicle Fleet Charging and Allocation Problem. The original formulation is a Mixed Integer Linear Program with continuous variables and inequality constraints. To separate inequality constraints that are difficult for quantum routines we use a decomposition in master and pricing problems: the former targets the assignment of vehicles to reservations and the latter suggests veh...
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Julian Eggert, Karsten Kreutz, Lina Gaumann, Jürgen Adamy , "How do Drivers take a Curve? The Comfort Factor Model (CMF)", 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), issue ITSC 2023, pp. 2598-2604, 2023.
AbstractHuman drivers are quite able to anticipate the dynamics that will occur in a curve and adjust their velocity accordingly. However, inapropriate speed estimation in curves is frequent in accidents, either by misjudement of own speed or misestimation of the curve speed of others. In this paper, we investigate which are the major factors which contribute to human curve speed adaptation. We investigate this empirically by extracting undisturbed curve...
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