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Nivesh Dommaraju, Mariusz Bujny, Stefan Menzel, Markus Olhofer, Fabian Duddeck , "Deep Neural Networks For Learning Geometric Features In Topology Optimization", ECCOMAS Congress 2020, 2021.
AbstractTopology Optimization (TO) redistributes material in a defined design space to provide optimal designs for multiple objectives under prescribed constraints. In the early design phase, due to flexibility in optimization constraints or boundary conditions, TO can be used to generate a large dataset of different design concepts. A few of the resulting designs can then be picked for further analysis of requirements not considered in the TO. Since the...
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Fabio Muratore , "Randomizing Physic Simulations for Robot Learning", TU Darmstadt, 2021.
AbstractThe ability to mentally evaluate variations of the future may well be the key to intelligence. Combined with the ability to reason, it makes humans excellent at handling new and complex situations. If we want robots to solve varying tasks autonomously, we need to endow them with such kind of ‘mental rehearsal’. Physics simulations allow to predict how the environment will change depending on a sequence of actions. For example, robots can sim...
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Mariusz Bujny, Markus Olhofer, Nikola Aulig, Fabian Duddeck , "Topology Optimization of 3D‑printed joints under crash loads using Evolutionary Algorithms", Structural and Multidisciplinary Optimization, 2021.
AbstractIn order to take full advantage of the enormous design freedom offered by Additive Manufacturing (AM) technologies, the use of Topology Optimization (TO) methods becomes essential. Although TO is well-established in many disciplines, the problems in vehicle crashworthiness pose severe difficulties for standard, gradient-based approaches, due to high noisiness, multi-modality, and discontinuous nature of the nonlinear simulation responses consider...
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Viktor Losing, Lydia Fischer, Jörg Deigmöller , "Extraction of Common-Sense Relations from Procedural Task Instructions using BERT", International Global Wordnet Conference, 2021.
AbstractManipulation-relevant common-sense knowledge is crucial to support action-planning for complex tasks. In particular, instrumentality information of what can be done with certain tools can be used to limit the search space which is growing exponentially with the number of viable options. Typical sources for such knowledge, structured common-sense knowledge bases such as ConceptNet or WebChild, provide a limited amount of information which also var...
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Hendric Voss, Heiko Wersing, Stefan Kopp , "Addressing data sparsity by combining unsupervised and semi-supervised learning for multimodal user state recognition", ACM International Conference on Multimodal Interaction (ICMI) Workshops, 2021.
AbstractDetecting mental states of human users is crucial for the develop- ment of cooperative and intelligent robots, as it enables the robot to understand the user’s intentions and desires. Despite their im- portance, it is difficult to obtain a large amount of high quality data for training automatic recognition algorithms as the time and effort required to collect and label such data is prohibitively high. In this paper we present a multimodal ...
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Pouya Aghaei-Pour, Tobias Rodemann, Jussi Hakanen, Kaisa Miettinen , "Surrogate Assisted Interactive Multiobjective Optimization in Energy System Design of Buildings", Optimization and Engineering, 2021.
AbstractIn this paper, we develop a novel evolutionary interactive method called interactive K-RVEA, which is suitable for computationally expensive problems. We use surrogate models to replace the original expensive objective functions in order to reduce the computation time. On the other hand, the decision maker should work with the solutions that are evaluated with the original objective functions. Therefore, we propose a novel model management stra...
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Moritz Bühler, Jürgen Adamy, Thomas H Weisswange , "Theory of Mind Based Assistive Communication in Complex Human Robot Cooperation", arXiv, 2021.
AbstractWhen cooperating with a human, a robot should not only care about its environment and task but also develop an understanding of the partner’s reasoning. To support its human partner in complex tasks, the robot can share information that it knows. However simply communicating everything will annoy and distract humans since they might already be aware of and not all information is relevant in the current situation. The questions when and what type ...
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Chao Wang, Thomas H Weisswange, Matti Krüger , "Design concept for Prediction-Level Collaboration between Human and Real-world Automated Driving System", Automotive UI 2021 , 2021.
AbstractAlthough automated driving (AD) systems progress fast in recent years, there are still various corner cases that such systems cannot handle well especially for predicting the behavior of surrounding traffic. This may result in discomfort or even dangerous situations. Results from a previous Wizard-of-OZ study suggest that the collaboration between human and system at the prediction level can effectively enhance the experience and comfort of autom...
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Chao Wang, Stephan Hasler, Manuel Mühlig, Frank Joublin, Antonello Ceravola, Jörg Deigmöller, Lydia Fischer , "Designing Interaction for Multi-agent Cooperative System in an Office Environment ", ACM/IEEE International Conference on Human-Robot Interaction, 2021.
AbstractFuture intelligent system will involve various artificial agents, including mobile robots, smart home infrastructure or personal devices, which share data and collaborate with each other to serve users. Designing efficient interactions which can support users to express needs to such intelligent environments, supervise the collaboration of different entities and evaluate the outcomes, will be challengeable. This paper presents the design and impl...
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Thiago de Jesus de Araujo Rios, Bas van Stein, Stefan Menzel, Thomas Bäck, Bernhard Sendhoff, Patricia Wollstadt , "Feature Visualization for 3D Point Cloud Autoencoders", International Joint Conference on Neural Networks (IJCNN), 2020.
AbstractIn order to reduce the dimensionality of 3D point cloud representations, autoencoder architectures generate increasingly abstract, compressed features of the input data. Visualizing these features is central to understanding the learning process, however, while successful visualization techniques exist for neural networks applied to computer vision tasks, similar methods for geometric, especially non-Euclidean, input data are currently lacking. H...
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