Dr. Tomas Kulvicius
| Group(s): |
Neural Control and Robotics , Computer Vision , Neural Computation |
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| Email: | T.Kulvicius
@physik3.gwdg.de
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| Phone: | +49 551/ 39 10763 |
| Website: | Kulvicius |
Publications
Please click on the respective heading to sort the list accordingly
(e.g. click on 'year' to sort the list in descending order of year of publication).
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BibTeX:
@ARTICLE{kulviciusningtamosiunaite2012,
author = {Kulvicius, T and Ning, K and Tamosiunaite, M and Wörgötter, F},
title = {Joining Movement Sequences: Modified Dynamic Movement Primitives for Robotics Applications Exemplified on Handwriting},
journal = {IEEE Transactions on Robotics, PP(99), 1-13},
year = {2012}
}
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| 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 based on the modification of the original dynamic movement primitive (DMP) formulation. The new method can reproduce the target trajectory with high accuracy regarding both, position and velocity profile, and produces smooth and natural transitions in position as well as velocity space. The properties of the method are demonstrated by applying it to simulated handwriting generation 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 high potential for all robotics applications where trajectory joining is required | |||
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BibTeX:
@INPROCEEDINGS{tamosiunaitemarkelickulvicius2011,
author = {Tamosiunaite, M and Markelic, I and Kulvicius, T and Wörgötter, F},
title = {Generalizing objects by analyzing language},
booktitle = {2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids)},
year = {2011}
}
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BibTeX:
@INPROCEEDINGS{ningkulviciustamosiunaite2011,
author = {Ning, K and Kulvicius, T and Tamosiunaite, M and Wörgötter, F},
title = {Accurate Position and Velocity Control for Trajectories Based on Dynamic Movement Primitives},
booktitle = {2011 IEEE International Conference on Robotics and Automation (ICRA)},
year = {2011}
}
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| Abstract: This paper presents a novel method for trajectory generation based on dynamic movement primitives (DMPs) treated from a control theoretical perspective. We extended the key ideas from the original DMP formalism by introducing a velocity convergence mechanism in the reformulated system. Theoretical proof is given to guarantee its validity. The new method can deal with complex paths as a whole. Based on this, we can generate smooth trajectories with automatically generated transition zones, satisfy position- and velocity boundary conditions at start and endpoint with high precision, and support multiple via-point applications. Theoretic proof of this method and experiments are presented | |||
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BibTeX:
@INPROCEEDINGS{manoonpongkulviciuswoergoetter2011,
author = {Manoonpong, P and Kulvicius, T and Wörgötter, F and Kunze, L and Renjewski, D and Seyfarth, A},
title = {Compliant Ankles and Flat Feet for Improved Self-Stabilization and Passive Dynamics of the Biped Robot RunBot},
booktitle = {The 2011 IEEE-RAS International Conference on Humanoid Robots},
year = {2011}
}
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BibTeX:
@INPROCEEDINGS{kulviciusningtamosiunaite2011,
author = {Kulvicius, T and Ning, K and Tamosiunaite, M and Wörgötter, F},
title = {Modified dynamic movement primitives for joining movement sequences},
booktitle = {2011 IEEE International Conference on Robotics and Automation},
year = {2011}
}
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| Abstract: The generation of complex movement patterns, in particular in cases where one needs to smoothly and accurately join trajectories, is still a difficult problem in robotics. This paper presents a novel approach for joining of several dynamic movement primitives (DMPs) based on a modification of the original formulation for DMPs. The new method produces smooth and natural transitions in position as well as velocity space. The properties of the method are demonstrated by applying it to simulated handwriting generation implemented 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 trajectory learning and generation and its accuracy and modular character has potential for various robotics applications | |||
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BibTeX:
@INPROCEEDINGS{abramovkulviciuswoergoetter2011,
author = {Abramov, A and Kulvicius, T and Wörgötter, F and Dellen, B},
title = {Real-Time Image Segmentation on a GPU},
booktitle = {Facing the Multicore-Challenge},
year = {2011}
}
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| Abstract: Efficient segmentation of color images is important for many applications in computer vision. Non-parametric solutions are required in situations where little or no prior knowledge about the data is available. In this paper, we present a novel parallel image segmentation algorithm which segments images in real-time in a non-parametric way. The algo- rithm finds the equilibrium states of a Potts model in the superparamag- netic phase of the system. Our method maps perfectly onto the Graphics Processing Unit (GPU) architecture and has been implemented using the framework NVIDIA Compute Unified Device Architecture (CUDA). For images of 256 × 320 pixels we obtained a frame rate of 30 Hz that demonstrates the applicability of the algorithm to video-processing tasks in real-time1 | |||
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BibTeX:
@ARTICLE{kulviciuskolodziejskitamosiunaite2010,
author = {Kulvicius, T and Kolodziejski, C and Tamosiunaite, M and Porr, B and Wörgötter, F},
title = {Behavioral analysis of differential hebbian learning in closed-loop systems},
journal = {Biological Cybernetics},
year = {2010}
}
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| 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 50s, 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 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 | |||
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BibTeX:
@ARTICLE{markelickulviciustamosiunaite2009,
author = {Markelic, I and Kulvicius, T and Tamosiunaite, M and Wörgötter, F},
title = {Anticipatory Driving for a Robot-Car Based on Supervised Learning},
journal = {Lecture Notes in Computer Science: Anticipatory Behavior in Adaptive Learning Systems},
year = {2009}
}
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| Abstract: Using look ahead information and plan making improves hu- man driving. We therefore propose that also autonomously driving systems should dispose over such abilities. We adapt a machine learning approach, where the system, a car-like robot, is trained by an experienced driver by correlating visual input to human driving actions. The heart of the system is a database where look ahead sensory information is stored together with action sequences issued by the human supervi- sor. The result is a robot that runs at real-time and issues steering and velocity control in a human-like way. For steer we adapt a two-level ap- proach, where the result of the database is combined with an additional reactive controller for robust behavior. Concerning velocity control this paper makes a novel contribution which is the ability of the system to react adequatly to upcoming curves | |||
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BibTeX:
@ARTICLE{tamosiunaiteaingekulvicius2008,
author = {Tamosiunaite, M and Ainge, J and Kulvicius, T and Porr, B and Dudchenko, P and Wörgötter, F},
title = {Path-finding in real and simulated rats: assessing the influence of path characteristics on navigation learning},
journal = {Journal of Computational Neuroscience},
year = {2008}
}
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| Abstract: A large body of experimental evidence suggests that the hippocampal place field system is involved in reward based navigation learning in rodents. Reinforcement learning (RL) mechanisms have been used to model this, associating the state space in an RL-algorithm to the place-field map in a rat. The convergence properties of RL-algorithms are affected by the exploration patterns of the learner. Therefore, we first analyzed the path characteristics of freely exploring rats in a test arena. We found that straight path segments with mean length 23 cm up to a maximal length of 80 cm take up a significant proportion of the total paths. Thus, rat paths are biased as compared to random exploration. Next we designed a RL system that reproduces these specific path characteristics. Our model arena is covered by overlapping, probabilistically firing place fields (PF) of realistic size and coverage. Because convergence of RL-algorithms is also influenced by the state space characteristics, different PF-sizes and densities, leading to a different degree of overlap, were also investigated. The model rat learns finding a reward opposite to its starting point. We observed that the combination of biased straight exploration, overlapping coverage and probabilistic firing will strongly impair the convergence of learning. When the degree of randomness in the exploration is increased, convergence improves, but the distribution of straight path segments becomes unrealistic and paths become ‘wiggly’. To mend this situation without affecting the path characteristic two additional mechanisms are implemented: A gradual drop of the learned weights (weight decay) and path length limitation, which prevents learning if the reward is not found after some expected time. Both mechanisms limit the memory of the system and thereby counteract effects of getting trapped on a wrong path. When using these strategies individually divergent cases get substantially reduced and for some parameter settings no divergence was found anymore at all. Using weight decay and path length limitation at the same time, convergence is not much improved but instead time to convergence increases as the memory limiting effect is getting too strong. The degree of improvement relies also on the size and degree of overlap (coverage density) in the place field system. The used combination of these two parameters leads to a trade-off between convergence and speed to convergence. Thus, this study suggests that the role of the PF-system in navigation learning cannot be considered independently from the animals’ exploration pattern | |||
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BibTeX:
@ARTICLE{kulviciustamosiunaiteainge2008,
author = {Kulvicius, T and Tamosiunaite, M and Ainge, J and Dudchenko, P and Wörgötter, F},
title = {Odor supported place cell model and goal navigation in rodents},
journal = {Journal of Computational Neuroscience},
year = {2008}
}
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| Abstract: Experiments with rodents demonstrate that visual cues play an important role in the control of hippocampal place cells and spatial navigation. Never- theless, 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 environ- mental and self-generated olfactory cues, together with a mixed navigation strategy, improves goal directed navigation | |||
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BibTeX:
@ARTICLE{porrkulviciuswoergoetter2007,
author = {Porr, B and Kulvicius, T and Wörgötter, F},
title = {Improved stability and convergence with three factor learning},
journal = {Neurocomputing},
year = {2007}
}
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BibTeX:
@ARTICLE{manoonponggengporr2007a,
author = {Manoonpong, P and Geng, T and Porr, B and Kulvicius, T and Wörgötter, F},
title = {Adaptive, Fast Walking in a Biped Robot under Neuronal Control and Learning. Read Belorussion translation: http://sportsbettingspot.com/online/ploscompbiol-journal-be},
journal = {Public Library of Science Computational Biology (PLoS Comp. Biol.), 3(7), e134)},
year = {2007}
}
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BibTeX:
@ARTICLE{kulviciusporrwoergoetter2007,
author = {Kulvicius, T and Porr, B and Wörgötter, F},
title = {Development of Receptive Fields in a Closed-Loop Behavioural System},
journal = {Neurocomputing},
year = {2007}
}
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BibTeX:
@ARTICLE{kulviciusberndwoergoetter2007,
author = {Kulvicius, T and Bernd, P and Wörgötter, F},
title = {Chaining learning architectures in a simple closed-loop behavioural context},
journal = {Biol. Cybern},
year = {2007}
}
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BibTeX:
@INPROCEEDINGS{kulviciusgengporr2006,
author = {Kulvicius, T and Geng, T and Porr, B and Wörgötter, F},
title = {Speed Optimization of a 2D Walking Robot through STDP},
booktitle = {Dynamical principles for neuroscience and intelligennt biomimetic devices: EPFL LATSIS Symposium 2006},
year = {2006}
}
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