Kulvicius, Tomas

[["dc.bibliographiccitation.firstpage","145"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","IEEE Transactions on Robotics"],["dc.bibliographiccitation.lastpage","157"],["dc.bibliographiccitation.volume","28"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Ning, KeJun"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T09:13:47Z"],["dc.date.available","2018-11-07T09:13:47Z"],["dc.date.issued","2012"],["dc.description.abstract","The generation of complex movement patterns, in particular, in cases where one needs to smoothly and accurately join trajectories in a dynamic way, is an important problem in robotics. This paper presents a novel joining method that is based on the modification of the original dynamic movement primitive formulation. The new method can reproduce the target trajectory with high accuracy regarding both the position and the velocity profile and produces smooth and natural transitions in position space, as well as in velocity space. The properties of the method are demonstrated by its application to simulated handwriting generation, which are also shown on a robot, where an adaptive algorithm is used to learn trajectories from human demonstration. These results demonstrate that the new method is a feasible alternative for joining of movement sequences, which has a high potential for all robotics applications where trajectory joining is required."],["dc.identifier.doi","10.1109/TRO.2011.2163863"],["dc.identifier.isi","000300188300012"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27247"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.issn","1552-3098"],["dc.title","Joining Movement Sequences: Modified Dynamic Movement Primitives for Robotics Applications Exemplified on Handwriting"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","S1053811921008077"],["dc.bibliographiccitation.firstpage","118534"],["dc.bibliographiccitation.journal","NeuroImage"],["dc.bibliographiccitation.volume","243"],["dc.contributor.author","Pomp, Jennifer"],["dc.contributor.author","Heins, Nina"],["dc.contributor.author","Trempler, Ima"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Mecklenbrauck, Falko"],["dc.contributor.author","Wurm, Moritz F."],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.author","Schubotz, Ricarda I."],["dc.date.accessioned","2021-10-01T09:57:38Z"],["dc.date.available","2021-10-01T09:57:38Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1016/j.neuroimage.2021.118534"],["dc.identifier.pii","S1053811921008077"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89881"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-469"],["dc.relation.issn","1053-8119"],["dc.title","Touching events predict human action segmentation in brain and behavior"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","92"],["dc.bibliographiccitation.journal","Robotics and Autonomous Systems"],["dc.bibliographiccitation.lastpage","107"],["dc.bibliographiccitation.volume","119"],["dc.contributor.author","Lüddecke, Timo"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Wörgötter, Florentin Andreas"],["dc.date.accessioned","2019-07-22T14:52:54Z"],["dc.date.available","2019-07-22T14:52:54Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1016/j.robot.2019.05.005"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61837"],["dc.language.iso","en"],["dc.relation.issn","0921-8890"],["dc.title","Context-based affordance segmentation from 2D images for robot actions"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","103854"],["dc.bibliographiccitation.journal","Research in Developmental Disabilities"],["dc.bibliographiccitation.volume","110"],["dc.contributor.author","Silva, Nelson"],["dc.contributor.author","Zhang, Dajie"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Gail, Alexander"],["dc.contributor.author","Barreiros, Carla"],["dc.contributor.author","Lindstaedt, Stefanie"],["dc.contributor.author","Kraft, Marc"],["dc.contributor.author","Bölte, Sven"],["dc.contributor.author","Poustka, Luise"],["dc.contributor.author","Nielsen-Saines, Karin"],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.author","Einspieler, Christa"],["dc.contributor.author","Marschik, Peter B."],["dc.date.accessioned","2021-04-14T08:28:50Z"],["dc.date.available","2021-04-14T08:28:50Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1016/j.ridd.2021.103854"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82720"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.issn","0891-4222"],["dc.title","The future of General Movement Assessment: The role of computer vision and machine learning – A scoping review"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","4013"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Networks and Learning Systems"],["dc.bibliographiccitation.lastpage","4025"],["dc.bibliographiccitation.volume","32"],["dc.contributor.author","Thor, Mathias"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Manoonpong, Poramate"],["dc.date.accessioned","2021-10-01T09:57:58Z"],["dc.date.available","2021-10-01T09:57:58Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1109/TNNLS.2020.3016523"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89955"],["dc.notes.intern","DOI Import GROB-469"],["dc.relation.eissn","2162-2388"],["dc.relation.issn","2162-237X"],["dc.title","Generic Neural Locomotion Control Framework for Legged Robots"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","Robotics and Autonomous Systems"],["dc.bibliographiccitation.lastpage","11"],["dc.bibliographiccitation.volume","94"],["dc.contributor.author","Herzog, Sebastian K."],["dc.contributor.author","Woergoetter, Florentin"],["dc.contributor.author","Kulvicius, Tomas"],["dc.date.accessioned","2018-11-07T10:21:54Z"],["dc.date.available","2018-11-07T10:21:54Z"],["dc.date.issued","2017"],["dc.description.abstract","Trajectory generation methods play an important role in robotics since they are essential for the execution of actions. In this paper we present a novel trajectory generation method for generalization of accurate movements with boundary conditions. Our approach originates from optimal control theory and is based on a second order dynamic system. We evaluate our method and compare it to the state of the art movement generation methods in both simulations and real robot experiments. We show that the new method is very compact in its representation and can reproduce reference trajectories with zero error. Moreover, it has most of the features of the state of the art movement generation methods such as robustness to perturbations and generalization to new position and velocity boundary conditions. We believe that, due to these features, our method may have potential for robotic applications where high accuracy is required paired with flexibility, for example, in modern industrial robotic applications, where more flexibility will be demanded as well as in medical robotics. (C) 2017 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.robot.2017.04.006"],["dc.identifier.isi","000404201700001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/42183"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Elsevier Science Bv"],["dc.relation.issn","1872-793X"],["dc.relation.issn","0921-8890"],["dc.title","Generation of movements with boundary conditions based on optimal control theory"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1450"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Robotics and Autonomous Systems"],["dc.bibliographiccitation.lastpage","1459"],["dc.bibliographiccitation.volume","61"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Biehl, Martin"],["dc.contributor.author","Aein, Mohamad Javad"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T09:17:04Z"],["dc.date.available","2018-11-07T09:17:04Z"],["dc.date.issued","2013"],["dc.description.abstract","Since several years dynamic movement primitives (DMPs) are more and more getting into the center of interest for flexible movement control in robotics. In this study we introduce sensory feedback together with a predictive learning mechanism which allows tightly coupled dual-agent systems to learn an adaptive, sensor-driven interaction based on DMPs. The coupled conventional (no-sensors, no learning) DMP-system automatically equilibrates and can still be solved analytically allowing us to derive conditions for stability. When adding adaptive sensor control we can show that both agents learn to cooperate. Simulations as well as real-robot experiments are shown. Interestingly, all these mechanisms are entirely based on low level interactions without any planning or cognitive component. (C) 2013 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.robot.2013.07.009"],["dc.identifier.isi","000328178200016"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/28076"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Bv"],["dc.relation.issn","1872-793X"],["dc.relation.issn","0921-8890"],["dc.title","Interaction learning for dynamic movement primitives used in cooperative robotic tasks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2005"],["dc.bibliographiccitation.issue","10-12"],["dc.bibliographiccitation.journal","Neurocomputing"],["dc.bibliographiccitation.lastpage","2008"],["dc.bibliographiccitation.volume","70"],["dc.contributor.author","Porr, Bernd"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T11:02:06Z"],["dc.date.available","2018-11-07T11:02:06Z"],["dc.date.issued","2007"],["dc.description.abstract","Donald Hebb postulated that if neurons fire together they wire together. However, Hebbian learning is inherently unstable because synaptic weights will self-amplify themselves: the more a synapse drives a postsynaptic cell the more the synaptic weight will grow. We present a new biologically realistic way of showing how to stabilise synaptic weights by introducing a third factor which switches learning on or off so that self-amplification is minimised. The third factor can be identified by the activity of dopaminergic neurons in ventral tegmental area which leads to a new interpretation of the dopamine signal which goes beyond the classical prediction error hypothesis. (c) 2006 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.neucom.2006.10.137"],["dc.identifier.isi","000247215300077"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/51298"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Bv"],["dc.publisher.place","Amsterdam"],["dc.relation.conference","15th Annual Computational Neuroscience Meeting"],["dc.relation.eventlocation","Edinburgh, SCOTLAND"],["dc.relation.issn","0925-2312"],["dc.title","Improved stability and convergence with three factor learning"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2046"],["dc.bibliographiccitation.issue","10-12"],["dc.bibliographiccitation.journal","Neurocomputing"],["dc.bibliographiccitation.lastpage","2049"],["dc.bibliographiccitation.volume","70"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Porr, Bernd"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T11:02:06Z"],["dc.date.available","2018-11-07T11:02:06Z"],["dc.date.issued","2007"],["dc.description.abstract","Recently it has been pointed out that in simple animals like flies a motor neuron can have a visual receptive field [H.G. Krapp, S.J. Huston, Encoding self-motion: From visual receptive fields to motor neuron response maps, in: H. Zimmermann, K. Kriegistein (Eds.), Proceedings of the sixth Meeting of the German Neuroscience Society/30th Gottingen Neurobiology Conference 2005, Gottingen, 2005, p. S16-3] [4]. Such receptive fields directly generate behaviour which, through closing the perception-action loop, will feed back to the sensors again. In more complex animals an increasingly complex hierarchy of visual receptive fields exists from early to higher visual areas, where visual input becomes more and more indirect. Here we will show that it is possible to develop receptive fields in simple behavioural systems by ways of a temporal sequence learning algorithm. The main goal is to demonstrate that learning generates stable behaviour and that the resulting receptive fields are also stable as soon as the newly learnt behaviour is successful. (c) 2006 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.neucom.2006.10.132"],["dc.identifier.isi","000247215300085"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/51299"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Bv"],["dc.publisher.place","Amsterdam"],["dc.relation.conference","15th Annual Computational Neuroscience Meeting"],["dc.relation.eventlocation","Edinburgh, SCOTLAND"],["dc.relation.issn","0925-2312"],["dc.title","Development of receptive fields in a closed-loop behavioural system"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","jcpp.13650"],["dc.bibliographiccitation.journal","Journal of Child Psychology and Psychiatry"],["dc.contributor.author","Schulte‐Rüther, Martin"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Stroth, Sanna"],["dc.contributor.author","Wolff, Nicole"],["dc.contributor.author","Roessner, Veit"],["dc.contributor.author","Marschik, Peter B."],["dc.contributor.author","Kamp‐Becker, Inge"],["dc.contributor.author","Poustka, Luise"],["dc.date.accessioned","2022-09-01T09:50:42Z"],["dc.date.available","2022-09-01T09:50:42Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1111/jcpp.13650"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113781"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-597"],["dc.relation.eissn","1469-7610"],["dc.relation.issn","0021-9630"],["dc.rights.uri","http://creativecommons.org/licenses/by-nc-nd/4.0/"],["dc.title","Using machine learning to improve diagnostic assessment of\n ASD\n in the light of specific differential and co‐occurring diagnoses"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]