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Fabio Muratore, Theo Gruner, Florian Wiese, Boris Belousov, Michael Gienger, Jan Peters , "Neural Posterior Domain Randomization", Conference on Robbot Learning (CorL), 2021.
AbstractCombining domain randomization and reinforcement learning is a widely used approach to train control policies that can bridge the gap between simulation and reality. However, existing methods make avoidable assumptions. Typically, one type of probability distribution (e.g., normal or uniform) is chosen a beforehand for every domain parameter. Another common assumption is the differentiability of the simulator. These design decisions simplif...
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Jonathan Jakob, Martina Hasenjäger, Barbara Hammer , "On the suitability of incremental learning for regression tasks in exoskeleton control", IEEE Symposium on Computational Intelligence in Data Mining (CIDM), 2021.
AbstractIn recent times, a new generation of modern exoskeleton robots has come into existence that aims to utilize machine learning to learn the specific needs and preferences of its users. A simple way to facilitate personalization of an exoskeleton to the end user is to make use of incremental algorithms that keep learning throughout their deployment. However, it is not clear, if any standard algorithms are fast enough to keep pace with sudden change ...
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Manuel Dietrich , "Addressing Inequal Risk Exposure in the Development of Automated Vehicles ", Ethics and Information Technology, vol. 23, pp. 727–738, 2021.
AbstractAutomated vehicles (AVs) are expected to operate on public roads, together with non-automated vehicles and other road users such as pedestrians or bicycles. Recent ethical reports and guidelines raise worries that AVs will introduce injustice or reinforce existing social inequalities in road traffic. One major injustice concern in today’s traffic is that different types of road users are exposed differently to risks of corporal harm. In the first...
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Karsten Kreutz and Julian Eggert , "Analysis of a Generalized Intelligent Driver Model for merging situations", IEEE Intelligent Vehicle Symposium 2021, pp. 34-41, 2021.
AbstractIn this paper, we analyze an extension of the Intelligent Driver Model (IDM) for its application on single situation prediction in merging situations. For this purpose, we first extend the original, longitudinal single car following IDM with several terms. First, we include a consideration of more than a single leading car to be able to deal with pressure from back, as required for anticipatory acceleration. Second, we use a virtual projection of...
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Francesco Romagnoli , "Electric load time series forecasting and relative predictions on simulation model ", UNIVERSITA’ POLITECNICA DELLE MARCHE, ANCONA, IT, 2021.
AbstractTime 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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Karsten Kreutz and Julian Eggert , "Analysis of the Generalized Intelligent Driver Model (GIDM) for uncontrolled intersections", IEEE Intelligent Transportation Systems Conference (ITSC) 2021, pp. 3223-3230, 2021.
AbstractIn this paper, we propose and analyze a Generalized Intelligent Driver Model (GIDM) as an extension of the Intelligent Driver Model (IDM) for its applicability to model uncontrolled intersection scenarios. For this purpose, we extend the original longitudinal car-following IDM with several terms:(1) for anticipatory acceleration capabilities, we include the most nearby backward agent, (2) we consider cars on paths that will cross the own path by...
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Steffen Limmer, Fernando Lezama, Joao Soares, Zita Vale , "Coordination of Home Appliances for Demand Response: An Improved Optimization Model and Approach", IEEE Access, vol. 9, pp. 146183-146194, 2021.
AbstractHome appliances constitute an interesting source of flexibility for demand responseprograms. However, their control and coordination are challenging, since typically a high number of suchappliances has to be aggregated in order to provide a sufficient amount of flexibility. Thus, an efficient andscalable control approach is required. In a previous work, metaheuristic methods were evaluated for solvinga control problem, which considers t...
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Saif Sidhik , "An Adaptive Framework for Changing-Contact Robot Manipulation", University of Birmingham, University of Birmingham, 2021.
AbstractMany robot manipulation tasks require the robot to make and break contact with other objects in the environment. The interaction dynamics of such tasks vary markedly before and after contact. They are also strongly influenced by the nature and physical properties of the objects involved, i.e., by factors such as type of contact, surface friction, and applied force. Many industrial assembly tasks and human manipulation tasks, e.g., peg insertion...
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Saif Sidhik, Mohan Sridharan, Dirk Ruiken , "Towards a Framework for Changing-ContactRobot Manipulation", RSS 2021 - Workshop on Advancing Artificial Intelligence and Manipulation for Robotics: Understanding Gaps, Industry and Academic Perspectives, and Community Building, 2021.
AbstractMany robot manipulation tasks require the robot to make and break contact with objects and surfaces. The dynamics of such \textit{changing-contact} robot manipulation tasks are discontinuous when contact is made or broken, and continuous elsewhere. These discontinuities make it difficult to construct and use a single dynamics model or control strategy for any such task. We present a framework for smooth dynamics and control of such changing-conta...
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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.
AbstractScan 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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