Kulvicius, Tomas

[["dc.bibliographiccitation.firstpage","481"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Journal of Computational Neuroscience"],["dc.bibliographiccitation.lastpage","500"],["dc.bibliographiccitation.volume","25"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Ainge, James A."],["dc.contributor.author","Dudchenko, Paul A."],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T11:08:36Z"],["dc.date.available","2018-11-07T11:08:36Z"],["dc.date.issued","2008"],["dc.description.abstract","Experiments with rodents demonstrate that visual cues play an important role in the control of hippocampal place cells and spatial navigation. Nevertheless, rats may also rely on auditory, olfactory and somatosensory stimuli for orientation. It is also known that rats can track odors or self-generated scent marks to find a food source. Here we model odor supported place cells by using a simple feed-forward network and analyze the impact of olfactory cues on place cell formation and spatial navigation. The obtained place cells are used to solve a goal navigation task by a novel mechanism based on self-marking by odor patches combined with a Q-learning algorithm. We also analyze the impact of place cell remapping on goal directed behavior when switching between two environments. We emphasize the importance of olfactory cues in place cell formation and show that the utility of environmental and self-generated olfactory cues, together with a mixed navigation strategy, improves goal directed navigation."],["dc.description.sponsorship","Biotechnology and Biological Sciences Research Council [BB/C516079/1]"],["dc.identifier.doi","10.1007/s10827-008-0090-x"],["dc.identifier.isi","000259438100005"],["dc.identifier.pmid","18431616"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/3067"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/52823"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","1573-6873"],["dc.relation.issn","0929-5313"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Odor supported place cell model and goal navigation in rodents"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["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","255"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Biological Cybernetics"],["dc.bibliographiccitation.lastpage","271"],["dc.bibliographiccitation.volume","103"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Kolodziejski, Christoph"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Porr, Bernd"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T08:38:17Z"],["dc.date.available","2018-11-07T08:38:17Z"],["dc.date.issued","2010"],["dc.description.abstract","Understanding closed loop behavioral systems is a non-trivial problem, especially when they change during learning. Descriptions of closed loop systems in terms of information theory date back to the 1950s, however, there have been only a few attempts which take into account learning, mostly measuring information of inputs. In this study we analyze a specific type of closed loop system by looking at the input as well as the output space. For this, we investigate simulated agents that perform differential Hebbian learning (STDP). In the first part we show that analytical solutions can be found for the temporal development of such systems for relatively simple cases. In the second part of this study we try to answer the following question: How can we predict which system from a given class would be the best for a particular scenario? This question is addressed using energy, input/output ratio and entropy measures and investigating their development during learning. This way we can show that within well-specified scenarios there are indeed agents which are optimal with respect to their structure and adaptive properties."],["dc.identifier.doi","10.1007/s00422-010-0396-4"],["dc.identifier.isi","000281667700001"],["dc.identifier.pmid","20556620"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/5158"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/18732"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","0340-1200"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Behavioral analysis of differential hebbian learning in closed-loop systems"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Journal of Computational Neuroscience"],["dc.bibliographiccitation.volume","28"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Ainge, James A."],["dc.contributor.author","Dudchenko, Paul A."],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T08:42:48Z"],["dc.date.available","2018-11-07T08:42:48Z"],["dc.date.issued","2010"],["dc.format.extent","617"],["dc.identifier.doi","10.1007/s10827-010-0216-9"],["dc.identifier.isi","000278406500018"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6799"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/19787"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","0929-5313"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Odour supported place cell model and goal navigation in rodents (vol 25, pg 481, 2008)"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Journal of Computational Neuroscience"],["dc.bibliographiccitation.volume","28"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Ainge, James A."],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Porr, Bernd"],["dc.contributor.author","Dudchenko, Paul A."],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T08:42:48Z"],["dc.date.available","2018-11-07T08:42:48Z"],["dc.date.issued","2010"],["dc.format.extent","619"],["dc.identifier.doi","10.1007/s10827-010-0217-8"],["dc.identifier.isi","000278406500019"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6800"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/19788"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","0929-5313"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Path-finding in real and simulated rats: assessing the influence of path characteristics on navigation learning (vol 25, pg 562, 2008)"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Neurorobotics"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Herzog, Sebastian"],["dc.contributor.author","Lüddecke, Timo"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2021-04-14T08:29:51Z"],["dc.date.available","2021-04-14T08:29:51Z"],["dc.date.issued","2021"],["dc.description.abstract","Path planning plays a crucial role in many applications in robotics for example for planning an arm movement or for navigation. Most of the existing approaches to solve this problem are iterative, where a path is generated by prediction of the next state from the current state. Moreover, in case of multi-agent systems, paths are usually planned for each agent separately (decentralized approach). In case of centralized approaches, paths are computed for each agent simultaneously by solving a complex optimization problem, which does not scale well when the number of agents increases. In contrast to this, we propose a novel method, using a homogeneous, convolutional neural network, which allows generation of complete paths, even for more than one agent, in one-shot, i.e., with a single prediction step. First we consider single path planning in 2D and 3D mazes. Here, we show that our method is able to successfully generate optimal or close to optimal (in most of the cases \\u0026lt;10% longer) paths in more than 99.5% of the cases. Next we analyze multi-paths either from a single source to multiple end-points or vice versa. Although the model has never been trained on multiple paths, it is also able to generate optimal or near-optimal (\\u0026lt;22% longer) paths in 96.4 and 83.9% of the cases when generating two and three paths, respectively. Performance is then also compared to several state of the art algorithms."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2020"],["dc.identifier.doi","10.3389/fnbot.2020.600984"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17791"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83006"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1662-5218"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","One-Shot Multi-Path Planning Using Fully Convolutional Networks in a Comparison to Other Algorithms"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["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","1"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Networks and Learning Systems"],["dc.bibliographiccitation.lastpage","11"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Herzog, Sebastian"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Worgotter, Florentin"],["dc.date.accessioned","2022-01-11T14:05:57Z"],["dc.date.available","2022-01-11T14:05:57Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1109/TNNLS.2021.3089023"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/97785"],["dc.notes.intern","DOI-Import GROB-507"],["dc.relation.eissn","2162-2388"],["dc.relation.issn","2162-237X"],["dc.title","Finding Optimal Paths Using Networks Without Learning--Unifying Classical Approaches"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1179"],["dc.bibliographiccitation.issue","10-11"],["dc.bibliographiccitation.journal","The International Journal of Robotics Research"],["dc.bibliographiccitation.lastpage","1207"],["dc.bibliographiccitation.volume","38"],["dc.contributor.author","Tamosiunaite, Minija"],["dc.contributor.author","Aein, Mohamad Javad"],["dc.contributor.author","Braun, Jan Matthias"],["dc.contributor.author","Kulvicius, Tomas"],["dc.contributor.author","Markievicz, Irena"],["dc.contributor.author","Kapociute-Dzikiene, Jurgita"],["dc.contributor.author","Valteryte, Rita"],["dc.contributor.author","Haidu, Andrei"],["dc.contributor.author","Chrysostomou, Dimitrios"],["dc.contributor.author","Ridge, Barry"],["dc.contributor.author","Krilavicius, Tomas"],["dc.contributor.author","Vitkute-Adzgauskiene, Daiva"],["dc.contributor.author","Beetz, Michael"],["dc.contributor.author","Madsen, Ole"],["dc.contributor.author","Ude, Ales"],["dc.contributor.author","Krüger, Norbert"],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2020-12-10T18:38:25Z"],["dc.date.available","2020-12-10T18:38:25Z"],["dc.date.issued","2019"],["dc.description.abstract","Human beings can generalize from one action to similar ones. Robots cannot do this and progress concerning information transfer between robotic actions is slow. We have designed a system that performs action generalization for manipulation actions in different scenarios. It relies on an action representation for which we perform code-snippet replacement, combining information from different actions to form new ones. The system interprets human instructions via a parser using simplified language. It uses action and object names to index action data tables (ADTs), where execution-relevant information is stored. We have created an ADT database from three different sources (KUKA LWR, UR5, and simulation) and show how a new ADT is generated by cutting and recombining data from existing ADTs. To achieve this, a small set of action templates is used. After parsing a new instruction, index-based searching finds similar ADTs in the database. Then the action template of the new action is matched against the information in the similar ADTs. Code snippets are extracted and ranked according to matching quality. The new ADT is created by concatenating code snippets from best matches. For execution, only coordinate transforms are needed to account for the poses of the objects in the new scene. The system was evaluated, without additional error correction, using 45 unknown objects in 81 new action executions, with 80% success. We then extended the method including more detailed shape information, which further reduced errors. This demonstrates that cut \\u0026 recombine is a viable approach for action generalization in service robotic applications."],["dc.description.sponsorship","European Community’s Seventh Framework Programme FP7 ICT-2011.2.1, Cognitive Systems and Robotics"],["dc.identifier.doi","10.1177/0278364919865594"],["dc.identifier.eissn","1741-3176"],["dc.identifier.issn","0278-3649"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77309"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.publisher","SAGE Publications"],["dc.relation.eissn","1741-3176"],["dc.relation.issn","0278-3649"],["dc.rights","http://creativecommons.org/licenses/by-nc/4.0/"],["dc.title","Cut & recombine: reuse of robot action components based on simple language instructions"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]