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Stefan Menzel , "AI-supported Evolutionary Design Optimization for Engineering Applications", Invited Talk Hochschule Aalen, 2024.
AbstractMachine learning and data science successfully contribute to system design and optimisation from a variety of perspectives and on different granularity levels in industrial applications. Among them are aspects like revealing hidden information from data to increase optimisation efficiency, learning surrogate models of costly simulation data for fast optimisation runtime, transferring knowledge between tasks, or exploring deep learning architectur...
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Andreas Neofytou, Thiago de Jesus de Araujo Rios, Mariusz Bujny, Stefan Menzel, Hyunsun Alicia Kim , "Coupled Multidisciplinary Topology Optimization Methodology and Platform: Noise minimization in vehicle cabin using 3D level set topology optimization considering acoustic-structure interaction", HRI-EU, 2024.
AbstractThis report summarizes the third year research of the project Coupled Multidisciplinary Topology Optimization Methodology and Platform. The modularized level set topology optimization (LSTO) methodology developed in the previous years was applied here to solve a vibroacoustic optimization problem in a benchmarking 3D vehicle cabin considering acoustic-structure interaction. Our objective was to minimize the noise generated at different points in...
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Atikkhan Faridkhan Nilgar, Manuel Dietrich, Kristof Van Laerhoven , "Users’ Perception on Appropriateness of Robotic Coaching Assistant’s Disclosure Behaviors", Robot Trust for Symbiotic Societies (RTSS) workshop at ICRA 2024, 2024.
AbstractSocial robots have emerged as valuable contributors to individuals' well-being coaching. Notably, their integration into long-term human coaching trials shows particular promise, emphasizing a complementary role alongside human coaches rather than outright replacement. In this context, robots serve as supportive entities during coaching sessions, offering insights based on their knowledge about the client's well-being and activity. Traditionally,...
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Thomas Schmitt, Jens Engel, Martin Kopp, Tobias Rodemann , "Implicit Incorporation of Heuristics in MPC-Based Control of a Hydrogen Plant", IEEE Power Electronics, Smart Grid and Renewable Energy (PESGRE 2023), 2024.
AbstractThe replacement of fossil fuels in combination with an increasing share of renewable energy sources leads to an increased focus on decentralized microgrids. One option is the local production of green hydrogen in combination with fuel cell vehicles (FCVs). In this paper, we develop a control strategy based on Model Predictive Control (MPC) for an energy management system (EMS) of a hydrogen plant, which is currently under installation in Offenbac...
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Linda Spaa, van der, Jens Kober, Michael Gienger , "Learning Preferences for Intention-Aware Physical Human-Robot Cooperation", Autonomous Robots, vol. 48, 2024.
AbstractThe advent of collaborative robots allows humans and robots to cooperate in a direct and phys- ical way. While this leads to amazing new opportunities to create novel robotics applications, it is challenging to make the collaboration intuitive for the human. From a system’s perspec- tive, understanding the human intentions seems to be one promising way to get there. However, human behavior exhibits large variations between individuals, such a...
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Michael Gienger , "Human-Robot interaction with Large Language Models", Keynote at the 12th Human Agent Interaction Conference, Swansea University, UK , 2024.
AbstractThe recent breakthroughs in Generative AI offer fantastic opportunities to research novel concepts for intelligent embodied agents. In this keynote, I will introduce our findings in designing interactive robot agents that learn from humans, and that can exploit their acquired knowledge in different situations. We designed a Virtual Playground, in which we conducted user studies to understand the efficiency of robot curiosity as well as of multi-m...
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Michael Gienger , "Human-Robot interaction with Large Language Models", Institute for Safe Autonomy, University of York, 2024.
AbstractThe recent breakthroughs in Generative AI offer fantastic opportunities to research novel concepts for intelligent embodied agents. In this talk, I will introduce recent research in exploiting Large Language Models (LLMs) for robot task and motion planning. We combined reasoning, planning, and motion generation, and introduced a novel concept for correcting errors during planning and execution. I’ll show several results both in simulations and re...
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Christiane Wiebel and Patricia Wollstadt , "What is successfull cooperation in human-AI partnerships?", Deutsche Gesellschaft für Psychologie Kongress (DGPS 2024), 2024.
AbstractCooperation is omnipresent in nature, from simple organisms that live in symbiosis to the creation of complex human societies. Recent research in AI-driven technology has claimed that AI systems need to learn to engage in cooperative interactions with humans, as well, to be successfully adopted in the future (Dafoe, 2020, 2021). Accordingly, the development of AI-systems must turn to developing cooperation – or “teaming-intelligence” – rather tha...
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Ahmed Sadik, Muhammad Ashfaq, Niko Mäkitalo, Tommi Mikkonen, Muhammad Waseem , "Enhancing Holonic Architecture with Natural Language Processing for System of Systems", 19th International Conference on Software Technologies - ICSOFT 2024 , 2024.
AbstractThe complexity and dynamic nature of System of Systems (SoS) necessitate efficient communication mechanisms to ensure interoperability and collaborative functioning among constituent systems, termed holons. This paper proposes an innovative approach to enhance holon communication within SoS through the integration of Natural Language Processing (NLP) techniques. By leveraging the advances in NLP, such as Large Language Models (LLMs), our approac...
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Pavel Smirnov, Frank Joublin, Antonello Ceravola, Michael Gienger , "Generating consistent PDDL domains with Large Language Models", arXiv, 2024.
AbstractIn this paper we address the problem of automated PDDL-domains generation using state-of-art LLMs minimizing human involvement into the loop. We detect and classify types of syntactic and consistency mistakes LLM tends to do and propose an integrated approach for detecting and resolving them in automated manner. Efficiency of the approach in terms of number of mistakes detected/resolved and reduced generation time we demonstrate on a number of sc...
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