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Chao Wang, Stephan Hasler, Daniel Tanneberg, Felix Ocker, Frank Joublin, Antonello Ceravola, Jörg Deigmöller, Michael Gienger, "LAMI: Large Language Models for Multi-Modal Human-Robot Interaction (CHI'24 workshop position paper)", CHI 2024 workshop, 2024.

Abstract

This paper presents an innovative large language model (LLM)-based robotic system for enhancing multi-modal human-robot interaction (HRI). Traditional HRI systems relied on complex designs for intent estimation, reasoning, and behavior generation, which were resource-intensive. In contrast, our system empowers researchers and practitioners to regulate robot behavior through three key aspects: providing high-level linguistic guidance, creating "at...



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Chao Wang, Stephan Hasler, Daniel Tanneberg, Felix Ocker, Frank Joublin, Antonello Ceravola, Jörg Deigmöller, Michael Gienger, "LaMI: Large Language Models for Multi-Modal Human-Robot Interaction ", CHI 2024, 2024.

Abstract

In current approaches for designing human-robot interaction, engineers specialized in the field of robotics establish rules based on the context of an application scenario and a multimodal input from a user in order to define how the robot should react in the specific situation, and to generate an output accordingly. This represents a challenging task, as manually setting up the robot's interactive behavior in a specific situation is complex and ...



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Jan Leusmann, Chao Wang, Sven Mayer, "Comparing Rule-based and LLM based Methods to enable Active Robot Assistant Conversations", Workshop@CHI 2024: Building Trust in CUIs – From Design to Deployment , 2024.

Abstract

Human-robot interaction (HRI) has recently undergone major advancements. Robots are not only autonomous agents anymore, but the domain is shifting more and more towards collaborative settings. Advancements in different topics have enabled the possibility of this shift. Robots can now perform various tasks to support humans in their daily lives. However, in collaborative settings, communication is key. In human-human collaborative settings, we oft...



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Stephan Hasler, Daniel Tanneberg, Michael Gienger, "Efficient symbolic planning with views", arxiv.org, 2024.

Abstract

Robotic planning systems model spatial relations detailed as these are needed for manipulation tasks. In contrast to this, other physical attributes of objects and the effect of devices are usually oversimplified and expressed by abstract compound attributes, e.g., describing a piece of bread as toasted. This limits the ability of planners to find alternative solutions. We propose to break these compound attributes down into a shared set of eleme...



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Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer, "Alleviating Search Bias in Evolutionary Bayesian Optimization with Many Heterogeneous Objectives", IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. 54, no. 1, pp. 143-155, 2024.

Abstract

Multi-objective optimization problems whose objectives have different evaluation costs are commonly seen in the real world. Such problems are now known as multi-objective optimization problems with heterogeneous objectives (HE-MOPs). So far, however, only a few studies have been reported to address HE-MOPs, and most of them focus on bi-objective problems with one fast objective and one slow objective. In this work, we aim to deal with HE-MOPs hav...



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Thomas Schmitt, Jens Engel, 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.

Abstract

The 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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Mikel Garcia de Andoin, Alberto Bottarelli, Sebastian Schmitt, Philipp Hauke, Mikel Sanz, " Formulation of the Electric Vehicle Charging and Routing Problem for a Hybrid Quantum-Classical Search Space Reduction Heuristic", International Conference on Intelligent Transportation Systems, 2024.

Abstract

Combinatorial optimization problems have attracted much interest in the quantum computing community in the recent years as a potential testbed to showcase quantum advantage. In this paper, we show how to exploit multilevel carriers of quantum information -- qudits -- for the construction of algorithms for constrained quantum optimization. These systems have been recently introduced in the context of quantum optimization and they allow us to treat...



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Ahmed Sadik, Sebastian Brulin, Markus Olhofer, "CODING BY DESIGN: GPT-4 EMPOWERS AGILE MODEL DRIVEN DEVELOPMENT", MODELSWARD 2024 - The 12th International Conference on Model-Based Software and Systems Engineering, 2024.

Abstract

Generating code from a natural language using Large Language Models (LLMs) such as ChatGPT, seems groundbreaking. Yet, with more extensive use, it's evident that this approach has its own limitations. The inherent ambiguity of natural language proposes challenges to auto-generate synergistically structured artifacts that can be deployed. Model Driven Development (MDD) is therefore being highlighted in this research as a proper approach to overcom...



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, "CoPal: Planning Robot Actions using Large Language Models", Github website, 2024.

Abstract

Recent advances in the field of pretrained Large Language Models (LLM) made commonsense knowledge available "out of the box" for a vast range of scenarios including content generation, customer service, and voice assistants. The release of GPT-3.5 (known as ChatGPT) opened prospectives for building highly contextualizable conversational agents, capable to hold a dialog and reflect about various situations as well as on behalf of different social ...



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Linda Spaa, van der, "LEARNING HUMAN PREFERENCES FOR PHYSICAL HUMAN-ROBOT COOPERATION", TU Delft, 2024.

Abstract

PHYSICAL human-robot cooperation (pHRC) has the potential to combine human and robot strengths in a team that can achieve more than a human and a robot working on the task separately. However, how much of the potential can be realized depends on the quality of cooperation, in which awareness of the partner’s intention and preferences plays an important role. Preferences tend to be highly personal, and additionally de- pend on the cooperati...



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