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Fabio Muratore, Christian Eilers, Michael Gienger, Jan Peters, "Data-efficient Domain Randomization with Bayesian Optimization", IEEE Robotics and Automation Letters (RA-L), 2021.

Abstract

REFERENCE PROPOSALS: pub-4266 and pub-4169 When learning policies for robot control, the required real-world data is typically prohibitively expensive to acquire, so learning in simulation is a popular strategy. Unfortunately, such polices are often not transferable to the real world due to a mismatch between the simulation and reality, called ‘reality gap’. Domain randomization methods tackle this problem by randomizing the physics simulato...



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Theodoros Stouraitis, "A Dyadic Collaborative Manipulation Formalism for Optimizing Human-Robot Teaming", Edinburgh University, 2021.

Abstract

Dyadic collaborative Manipulation (DcM) is a term we use to refer to a team of two individuals, the agent and the partner, jointly manipulating an object. The two indi- viduals partner together to form a distributed system, augmenting their manipula- tion abilities. Effective collaboration between the two individuals during joint action depends on: (i) the breadth of the agent’s action repertoire, (ii) the level of model ac- quaintance bet...



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Patricia Wollstadt, Martina Hasenjäger, Christiane Wiebel, "Quantifying Predictability of Scan Path Data using Active Information Storage", Entropy, vol. 23, no. 2, pp. 167, 2021.

Abstract

Entropy-based measures are an important tool to study human gaze behavior under various conditions. Measures, such as the gaze transition entropy, are used to quantify the predictability of transitions between consecutive fixations. However, these measures do not account for temporal dependencies beyond interactions of order one. Therefore, we propose a novel approach to quantifying predictability in scanpath data by estimating the active informa...



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Francesco Romagnoli, "Electric load time series forecasting and relative predictions on simulation model ", UNIVERSITA’ POLITECNICA DELLE MARCHE, ANCONA, IT, 2021.

Abstract

Time series forecasting is an important area of machine learning because there are so many prediction problems that involve a time component. A normal machine learning dataset is a collection of observations, while a time series dataset adds an explicit order dependence between observations, represented by time dimension. This additional dimension is both a constraint and a structure that provides a source of addit...



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Christiane Wiebel, Matti Krüger, Patricia Wollstadt, "Measuring inter- and intra-individual differences in visual scan patterns in a driving simulator experiment using active information storage", PLOS ONE, vol. 16, no. 3, pp. e0248166, 2021.

Abstract

Scan pattern analysis has been discussed as a promising tool in the context of real-time gaze-based applications [1]. In particular, information-theoretic measures of scan path predictability, such as the gaze transition entropy (GTE), have been proposed for detecting relevant changes in user state or task demand [2]. These measures model scan patterns as first-order Markov chains, assuming that only the location of the previous fixation is predi...



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Duc Nguyen, Ewout Zwanenburg, Steffen Limmer, Wessel Luijben, Markus Olhofer, Thomas Bäck, "Fourier Transform and Machine Learning Techniques for Fault Detection and Diagnosis of Induction Motors", International Conference on Prognostics and Health Management, 2021.

Abstract

Induction motors are widely used in different industry areas and can experience various kinds of faults in stators and rotors. In general, fault detection and diagnosis techniques for induction motors can be supervised by measuring quantities such as noise, vibration, and temperature. The installation of mechanical sensors in order to assess the health conditions of a machine is typically only done for expensive or load-critical machines, where ...



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Mariusz Bujny, Muhammad Yousaf, Fabian Duddeck, "Similarity-based Topology Optimization for Crash and Statics", ECCOMAS Congress 2020, 2021.

Abstract

Topology Optimization (TO) [1] is an important technique that redistributes the material within a design domain to optimize certain objective functions under specified constraints. In industry, efficient gradient-based techniques using SIMP (Solid Isotropic Material with Penalization) interpolation with Optimality Criteria (OC) [1] or heuristic approaches such as Hybrid Cellular Automata (HCA) [2] are used to optimize structures without taking i...



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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.

Abstract

Topology 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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Viktor Losing, Lydia Fischer, Jörg Deigmöller, "Extraction of Common-Sense Relations from Procedural Task Instructions using BERT", International Global Wordnet Conference, 2021.

Abstract

Manipulation-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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Pouya Aghaei-Pour, Tobias Rodemann, Jussi Hakanen, Kaisa Miettinen, "Surrogate Assisted Interactive Multiobjective Optimization in Energy System Design of Buildings", Optimization and Engineering, 2021.

Abstract

In 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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