Search our Publications
Latest Publications
Ahmad Reza Cheraghi, Abdelrahman Abdelgalil, Kalman György Graffi , "Universal 2-Dimensional Terrain Marking for Autonomous Robot Swarms", 5th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS 2020), 2020.
AbstractThis research introduces the design and implementation of a universal systematic 2-dimensional terrain marking and coverage solution. Real world applications such as lawn mowing, mine detection, chemical spill clean-up, and humanitarian search and rescue missions can be automated by employing swarms of autonomous mobile robots to complete the task. In most of these real world applications, efficiency is of utmost importance especially when human ...
Download Bibtex file
Theodoros Stouraitis, Lei Yan, Joao Moura, Michael Gienger, Sethu Vijayakumar , "Multi-mode Trajectory Optimization for Impact-aware Manipulation", Robotics Science and Systems (RSS) Workshop: "Reacting to Contact: Enabling Transparent Interactions through Intelligent Sensing and Actuation", Online workshop, 2020.
AbstractThis is a workshop paper based on the publication pub-4275. The transition from free motion to contact is a challenging problem in robotics, in part due to its hybrid nature. Yet, disregarding the effects of impacts at the motion planning level might result in intractable impulsive contact forces. In this paper, we introduce an impact-aware multi-mode trajectory optimization (TO) method that comprises both hybrid dynamics and hybrid cont...
Download Bibtex file
Taizo Yoshikawa and Viktor Losing , "Machine Learning for Human Movement Understanding", Advanced Robotics, vol. 34, no. 13, 2020.
AbstractMain purpose of this project is to develop fundamental technology for assist robots to recover and maintain human motor skill and to extend scope of human activity. Our goal is to provide a system that adapts to its user’s personal behavior patterns in real-time. We aim to develop a continuous collaboration system between the physical assist robots and the user where both alternatively adjust to each other to maximize th...
Download Bibtex file
Sibghat Ullah, Zhao Xu, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck , "Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models", International Joint Conference on Neural Networks (IJCNN 2020), 2020.
AbstractClinical time series are known for irregular, highly-sporadic and strongly-complex structures and are consequently difficult to model by traditional state-space models. In this paper, we investigate the potential of applying variational recurrent neural networks (VRNNs) for forecasting clinical time series extracted from electronic health records (EHRs) of patients. Variational recurrent neural networks (VRNNs) combine recurrent neural networks (...
Download Bibtex file
Gan Ruan, Leandro L. Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao , "Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization", IEEE Congress on Evolutionary Computation (IEEE CEC), 2020.
AbstractTransfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is believed to be able to transfer useful information from one problem instance to help solving another related problem instance. This paper aims to study how effective transfer learning is in dynamic multi-objective optimization (DMO). Through computation time analysis of transfer learning, we show that the...
Download Bibtex file
Stephen Friess, Peter Tino, Stefan Menzel, Bernhard Sendhoff, Xin Yao , "Representing Experience in Continuous Evolutionary Optimisation through Problem-tailored Search Operators", IEEE Congress on Evolutionary Computation (IEEE CEC), 2020.
AbstractEvolutionary algorithms are a class of population-based metaheuristic methods partially inspired by natural evolution. Specifically, they rely on stochastic variation and selection processes to sequentially find optimal solutions of a function of interest. We attempt in this work to extract preferences in these stochastic evolutionary operators in form of empirical and improved distributions as basis for model-based mutation operators. The latter...
Download Bibtex file
Martin Weigel, Oliver Schön, Herbert Janßen , "Evaluation of Body-Worn FPCBs with Bluetooth Low Energy, Capacitive Touch, and Resistive Flex Sensing", Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers (UbiComp/ISWC ’20 Adjunct), 2020.
AbstractCommercially available flexible printed circuit boards (FPCBs) have the potential to embed electronics, connectivity, and interactivity into the same surface. This makes them an ideal platform for untethered and interactive wearable devices. However, we lack an understanding how well FPCB-based antennas and sensors perform when worn directly on the body. This work contributes an understanding by studying body-worn FPCBs in three technical eval...
Download Bibtex file
Chao Wang, Matti Krüger, Christiane Wiebel , "“Watch out!”: Prediction-Level Intervention for Automated Driving", AutomotiveUI 2020, 2020.
AbstractIt seems that autonomous driving systems are substituting human responsibilities in the driving task. However, this does not mean that vehicles should not interact with their driver anymore, even in case of full automation. One reason is that the automation is not yet advanced enough to predict other road user’s behavior in complex situations, which can lead to sub-optimal action choices, decrease comfort and user experience. In contrast, a human...
Download Bibtex file
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", Arxiv, Arxiv, 2020.
AbstractFuture intelligent system will involve very various types of artificial agents, such as mobile robots, smart home infrastructure or personal devices, which share data and collaborate with each other to execute certain tasks. Designing an efficient human-machine interface, which can support users to express needs to the system, supervise the collaboration progress of different entities and evaluate the result, will be challengeable. This paper pre...
Download Bibtex file
Manuel Rudolph , "Exploring and Benchmarking Quantum-assisted Neural Networks with Qubit Layers", University of Heidelberg, 2020.
AbstractThe aim of this work is to explore practical implementations of Quantum and Quantum-assisted Machine Learning algorithms and benchmark potential bene- fits of utilizing quantum phenomena in Quantum-assisted Neural Networks with qubit layers. Two known approaches of generative Quantum Machine Learning algorithms are revised to demonstrate the encoding capability and sampling benefits of qubits. As one possible extension of those generative m...
Download Bibtex file Per Mail Request
1 ... 65 66 67 68 69 70 ... 183
Follow us
