Timo Nachstedt

Group(s): Neural Control and Robotics
Email:
timo.nachstedt@phys.uni-goettingen.de
Phone: +49 551/ 39 10762
Room: E.01.105
Website: Nachstedt

Global QuickSearch:   Matches: 0

Search Settings

    Author / Editor / Organization
    Year
    Title
    Journal / Proceedings / Book
    Nachstedt, T. and Wörgötter, F. and Manoonpong, P. and Ariizumi, R. and Ambe, Y. and Matsuno, F. (2013).
    Adaptive neural oscillators with synaptic plasticity for locomotion control of a snake-like robot with screw-drive mechanism. IEEE International Conference on Robotics and Automation ICRA, 3389-3395. DOI: 10.1109/ICRA.2013.6631050.
    BibTeX:
    @inproceedings{nachstedtwoergoettermanoonpong2013,
      author = {Nachstedt, T. and Wörgötter, F. and Manoonpong, P. and Ariizumi, R. and Ambe, Y. and Matsuno, F.},
      title = {Adaptive neural oscillators with synaptic plasticity for locomotion control of a snake-like robot with screw-drive mechanism},
      pages = {3389-3395},
      booktitle = {IEEE International Conference on Robotics and Automation ICRA},
      year = {2013},
      location = {Kralsruhe (Germany)},
      month = {May 6-10},
      doi = {10.1109/ICRA.2013.6631050},
      abstract = {Central pattern generators (CPGs) play a crucial role for animal locomotion control. They can be entrained by sensory feedback to induce proper rhythmic patterns and even store the entrained patterns through connection weights. Inspired by this biological finding, we use four adaptive neural oscillators with synaptic plasticity as CPGs for locomotion control of our real snake-like robot with screw-drive mechanism. Each oscillator consists of only three neurons and uses adaptive mechanisms based on frequency adaptation and Hebbian-type learning rules. It autonomously generates proper periodic patterns for the robot locomotion and can be entrained by sensory feedback to memorize the patterns. The adaptive CPG system in conjunction with a simple control strategy enables the robot to perform self-tuning behavior which is robust against short-time perturbations. The generated behavior is also energy efficient. In addition, the robot can also cope with corners as well as move through a complex environment with obstacles.}}
    Abstract: Central pattern generators (CPGs) play a crucial role for animal locomotion control. They can be entrained by sensory feedback to induce proper rhythmic patterns and even store the entrained patterns through connection weights. Inspired by this biological finding, we use four adaptive neural oscillators with synaptic plasticity as CPGs for locomotion control of our real snake-like robot with screw-drive mechanism. Each oscillator consists of only three neurons and uses adaptive mechanisms based on frequency adaptation and Hebbian-type learning rules. It autonomously generates proper periodic patterns for the robot locomotion and can be entrained by sensory feedback to memorize the patterns. The adaptive CPG system in conjunction with a simple control strategy enables the robot to perform self-tuning behavior which is robust against short-time perturbations. The generated behavior is also energy efficient. In addition, the robot can also cope with corners as well as move through a complex environment with obstacles.
    Review:
    Nachstedt, T. and Wörgötter, F. and Manoonpong, P. (2012).
    Adaptive Neural Oscillator with Synaptic Plasticity Enabling Fast Resonance Tuning. Artificial Neural Networks and Machine Learning ICANN, 451-458, 7552. DOI: 10.1007/978-3-642-33269-2_57.
    BibTeX:
    @incollection{nachstedtwoergoettermanoonpong2012,
      author = {Nachstedt, T. and Wörgötter, F. and Manoonpong, P.},
      title = {Adaptive Neural Oscillator with Synaptic Plasticity Enabling Fast Resonance Tuning},
      pages = {451-458},
      booktitle = {Artificial Neural Networks and Machine Learning ICANN},
      year = {2012},
      volume= {7552},
      editor = {Villa, AlessandroE.P. and Duch, Wodzislaw and Irdi, Peter and Masulli, Francesco and Palm, Günther},
      publisher = {Springer Berlin Heidelberg},
      series = {Lecture Notes i},
      url = {http://dx.doi.org/10.1007/978-3-642},
      doi = {10.1007/978-3-642-33269-2_57},
      abstract = {Rhythmic neural circuits play an important role in biological systems in particular in motion generation. They can be entrained by sensory feedback to induce rhythmic motion at a natural frequency, leading to energy-efficient motion. In addition, such circuits can even store the entrained rhythmical patterns through connection weights. Inspired by this, we introduce an adaptive discrete-time neural oscillator system with synaptic plasticity. The system consists of only three neurons and uses adaptive mechanisms based on frequency adaptation and Hebbian-type learning rules. As a result, it autonomously generates periodic patterns and can be entrained by sensory feedback to memorize a pattern. Using numerical simulations we show that this neural system possesses fast and precise convergence behaviour within a wide target frequency range. We use resonant tuning of a pendulum as a simple system for demonstrating possible applications of the adaptive oscillator network.}}
    Abstract: Rhythmic neural circuits play an important role in biological systems in particular in motion generation. They can be entrained by sensory feedback to induce rhythmic motion at a natural frequency, leading to energy-efficient motion. In addition, such circuits can even store the entrained rhythmical patterns through connection weights. Inspired by this, we introduce an adaptive discrete-time neural oscillator system with synaptic plasticity. The system consists of only three neurons and uses adaptive mechanisms based on frequency adaptation and Hebbian-type learning rules. As a result, it autonomously generates periodic patterns and can be entrained by sensory feedback to memorize a pattern. Using numerical simulations we show that this neural system possesses fast and precise convergence behaviour within a wide target frequency range. We use resonant tuning of a pendulum as a simple system for demonstrating possible applications of the adaptive oscillator network.
    Review:
    Chatterjee, S. and Nachstedt, T. and Wörgötter, F. and Tamosiunaite, M. and Manoonpong, P. and Enomoto, Y. and Ariizumi, R. and Matsuno, F. (2014).
    Reinforcement learning approach to generate goal-directed locomotion of a snake-like robot with screw-drive units. 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD), 1-7. DOI: 10.1109/RAAD.2014.7002234.
    BibTeX:
    @inproceedings{chatterjeenachstedtwoergoetter2014,
      author = {Chatterjee, S. and Nachstedt, T. and Wörgötter, F. and Tamosiunaite, M. and Manoonpong, P. and Enomoto, Y. and Ariizumi, R. and Matsuno, F.},
      title = {Reinforcement learning approach to generate goal-directed locomotion of a snake-like robot with screw-drive units},
      pages = {1-7},
      booktitle = {23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD)},
      year = {2014},
      month = {Sept},
      doi = {10.1109/RAAD.2014.7002234},
      abstract = {In this paper we apply a policy improvement algorithm called Policy Improvement with Path Integrals (PI2) to generate goal-directed locomotion of a complex snake-like robot with screw-drive units. PI2 is numerically simple and has an ability to deal with high dimensional systems. Here, this approach is used to find proper locomotion control parameters, like joint angles and screw-drive velocities, of the robot. The learning process was achieved using a simulated robot and the learned parameters were successfully transferred to the real one. As a result the robot can locomote toward a given goal.}}
    Abstract: In this paper we apply a policy improvement algorithm called Policy Improvement with Path Integrals (PI2) to generate goal-directed locomotion of a complex snake-like robot with screw-drive units. PI2 is numerically simple and has an ability to deal with high dimensional systems. Here, this approach is used to find proper locomotion control parameters, like joint angles and screw-drive velocities, of the robot. The learning process was achieved using a simulated robot and the learned parameters were successfully transferred to the real one. As a result the robot can locomote toward a given goal.
    Review:
    Ambe, Y. and Nachstedt, T. and Manoonpong, P. and Wörgötter, F. and Aoi, S. and Matsuno, F. (2013).
    Stability analysis of a hexapod robot driven by distributed nonlinear oscillators with a phase modulation mechanism. IEEE International Conference on Intelligent Robots and Systems, 5087--5092. DOI: 10.1109/IROS.2013.6697092.
    BibTeX:
    @inproceedings{ambenachstedtmanoonpong2013,
      author = {Ambe, Y. and Nachstedt, T. and Manoonpong, P. and Wörgötter, F. and Aoi, S. and Matsuno, F.},
      title = {Stability analysis of a hexapod robot driven by distributed nonlinear oscillators with a phase modulation mechanism},
      pages = {5087--5092},
      booktitle = {IEEE International Conference on Intelligent Robots and Systems},
      year = {2013},
      month = {11},
      doi = {10.1109/IROS.2013.6697092},
      abstract = {In this paper, we investigated the dynamics of a hexapod robot model whose legs are driven by nonlinear oscillators with a phase modulation mechanism including phase resetting and inhibition. This mechanism changes the oscillation period of the oscillator depending solely on the timing of the foots contact. This strategy is based on observation of animals. The performance of the controller is evaluated using a physical simulation environment. Our simulation results show that the robot produces some stable gaits depending on the locomotion speed due to the phase modulation mechanism, which are simillar to the gaits of insects.}}
    Abstract: In this paper, we investigated the dynamics of a hexapod robot model whose legs are driven by nonlinear oscillators with a phase modulation mechanism including phase resetting and inhibition. This mechanism changes the oscillation period of the oscillator depending solely on the timing of the foots contact. This strategy is based on observation of animals. The performance of the controller is evaluated using a physical simulation environment. Our simulation results show that the robot produces some stable gaits depending on the locomotion speed due to the phase modulation mechanism, which are simillar to the gaits of insects.
    Review:
    Chatterjee, S. and Nachstedt, T. and Tamosiunaite, M. and Wörgötter, F. and Enomoto, Y. and Ariizumi, R. and Matsuno, F. and Manoonpong, P. (2015).
    Learning and Chaining of Motor Primitives for Goal-Directed Locomotion of a Snake-Like Robot with Screw-Drive Units. International Journal of Advanced Robotic Systems, 12, 12. DOI: 10.5772/61621.
    BibTeX:
    @article{chatterjeenachstedttamosiunaite2015,
      author = {Chatterjee, S. and Nachstedt, T. and Tamosiunaite, M. and Wörgötter, F. and Enomoto, Y. and Ariizumi, R. and Matsuno, F. and Manoonpong, P.},
      title = {Learning and Chaining of Motor Primitives for Goal-Directed Locomotion of a Snake-Like Robot with Screw-Drive Units},
      journal = {International Journal of Advanced Robotic Systems},
      year = {2015},
      volume= {12},
      number = {12},
      doi = {10.5772/61621},
      abstract = {In this paper we apply a policy improvement algorithm called Policy Improvement with Path Integrals (PItextlesssuptextgreater2textless/suptextgreater) to generate goal-directed locomotion of a complex snake-like robot with screw-drive units. PItextlesssuptextgreater2textless/suptextgreater is numerically simple and has an ability to deal with high dimensional systems. Here, this approach is used to find proper locomotion control parameters, like joint angles and screw-drive velocities, of the robot. The learning process was achieved using a simulated robot and the learned parameters were successfully transferred to the real one. As a result the robot can locomote toward a given goal. © 2014 IEEE.}}
    Abstract: In this paper we apply a policy improvement algorithm called Policy Improvement with Path Integrals (PItextlesssuptextgreater2textless/suptextgreater) to generate goal-directed locomotion of a complex snake-like robot with screw-drive units. PItextlesssuptextgreater2textless/suptextgreater is numerically simple and has an ability to deal with high dimensional systems. Here, this approach is used to find proper locomotion control parameters, like joint angles and screw-drive velocities, of the robot. The learning process was achieved using a simulated robot and the learned parameters were successfully transferred to the real one. As a result the robot can locomote toward a given goal. © 2014 IEEE.
    Review:
    Nachstedt, T. and Tetzlaff, C. (2017).
    Working Memory Requires a Combination of Transient and Attractor-Dominated Dynamics to Process Unreliably Timed Inputs. Scientific Reports, 2473, 7, 1. DOI: 10.1038/s41598-017-02471-z.
    BibTeX:
    @article{nachstedttetzlaff2017,
      author = {Nachstedt, T. and Tetzlaff, C.},
      title = {Working Memory Requires a Combination of Transient and Attractor-Dominated Dynamics to Process Unreliably Timed Inputs},
      pages = {2473},
      journal = {Scientific Reports},
      year = {2017},
      volume= {7},
      number = {1},
      publisher = {Springer US},
      url = {http://www.nature.com/articles/s41598-017-02471-z},
      doi = {10.1038/s41598-017-02471-z},
      abstract = {Working memory stores and processes information received as a stream of continuously incoming stimuli. This requires accurate sequencing and it remains puzzling how this can be reliably achieved by the neuronal system as our perceptual inputs show a high degree of temporal variability. One hypothesis is that accurate timing is achieved by purely transient neuronal dynamics by contrast a second hypothesis states that the underlying network dynamics are dominated by attractor states. In this study, we resolve this contradiction by theoretically investigating the performance of the system using stimuli with differently accurate timing. Interestingly, only the combination of attractor and transient dynamics enables the network to perform with a low error rate. Further analysis reveals that the transient dynamics of the system are used to process information, while the attractor states store it. The interaction between both types of dynamics yields experimentally testable predictions and we show that this way the system can reliably interact with a timing-unreliable Hebbian-network representing long-term memory. Thus, this study provides a potential solution to the long-standing problem of the basic neuronal dynamics underlying working memory.}}
    Abstract: Working memory stores and processes information received as a stream of continuously incoming stimuli. This requires accurate sequencing and it remains puzzling how this can be reliably achieved by the neuronal system as our perceptual inputs show a high degree of temporal variability. One hypothesis is that accurate timing is achieved by purely transient neuronal dynamics by contrast a second hypothesis states that the underlying network dynamics are dominated by attractor states. In this study, we resolve this contradiction by theoretically investigating the performance of the system using stimuli with differently accurate timing. Interestingly, only the combination of attractor and transient dynamics enables the network to perform with a low error rate. Further analysis reveals that the transient dynamics of the system are used to process information, while the attractor states store it. The interaction between both types of dynamics yields experimentally testable predictions and we show that this way the system can reliably interact with a timing-unreliable Hebbian-network representing long-term memory. Thus, this study provides a potential solution to the long-standing problem of the basic neuronal dynamics underlying working memory.
    Review:
    Nachstedt, T. and Tetzlaff, C. and Manoonpong, P. (2017).
    Fast dynamical coupling enhances frequency adaptation of oscillators for robotic locomotion control. Frontiers in Neurorobotics, 1--14, 11. DOI: 10.3389/fnbot.2017.00014.
    BibTeX:
    @article{nachstedttetzlaffmanoonpong2017,
      author = {Nachstedt, T. and Tetzlaff, C. and Manoonpong, P.},
      title = {Fast dynamical coupling enhances frequency adaptation of oscillators for robotic locomotion control},
      pages = {1--14},
      journal = {Frontiers in Neurorobotics},
      year = {2017},
      volume= {11},
      url = {http://journal.frontiersin.org/article/10.3389/fnbot.2017.00014},
      doi = {10.3389/fnbot.2017.00014},
      abstract = {Rhythmic neural signals serve as basis of many brain processes, in particular of locomotion control and generation of rhythmic movements. It has been found that specific neural circuits, named central pattern generators (CPGs), are able to autonomously produce such rhythmic activities. In order to tune, shape and coordinate the produced rhythmic activity, CPGs require sensory feedback, i.e., external signals. Nonlinear oscillators are a standard model of CPGs and are used in various robotic applications. A special class of nonlinear oscillators are adaptive frequency oscillators (AFOs). AFOs are able to adapt their frequency toward the frequency of an external periodic signal and to keep this learned frequency once the external signal vanishes. AFOs have been successfully used, for instance, for resonant tuning of robotic locomotion control. However, the choice of parameters for a standard AFO is characterized by a trade-off between the speed of the adaptation and its precision and, additionally, is strongly dependent on the range of frequencies the AFO is confronted with. As a result, AFOs are typically tuned such that they require a comparably long time for their adaptation. To overcome the problem, here, we improve the standard AFO by introducing a novel adaptation mechanism based on dynamical coupling strengths. The dynamical adaptation mechanism enhances both the speed and precision of the frequency adaptation. In contrast to standard AFOs, in this system, the interplay of dynamics on short and long time scales enables fast as well as precise adaptation of the oscillator for a wide range of frequencies. Amongst others, a very natural implementation of this mechanism is in terms of neural networks. The proposed system enables robotic applications which require fast retuning of locomotion control in order to react to environmental changes or conditions.}}
    Abstract: Rhythmic neural signals serve as basis of many brain processes, in particular of locomotion control and generation of rhythmic movements. It has been found that specific neural circuits, named central pattern generators (CPGs), are able to autonomously produce such rhythmic activities. In order to tune, shape and coordinate the produced rhythmic activity, CPGs require sensory feedback, i.e., external signals. Nonlinear oscillators are a standard model of CPGs and are used in various robotic applications. A special class of nonlinear oscillators are adaptive frequency oscillators (AFOs). AFOs are able to adapt their frequency toward the frequency of an external periodic signal and to keep this learned frequency once the external signal vanishes. AFOs have been successfully used, for instance, for resonant tuning of robotic locomotion control. However, the choice of parameters for a standard AFO is characterized by a trade-off between the speed of the adaptation and its precision and, additionally, is strongly dependent on the range of frequencies the AFO is confronted with. As a result, AFOs are typically tuned such that they require a comparably long time for their adaptation. To overcome the problem, here, we improve the standard AFO by introducing a novel adaptation mechanism based on dynamical coupling strengths. The dynamical adaptation mechanism enhances both the speed and precision of the frequency adaptation. In contrast to standard AFOs, in this system, the interplay of dynamics on short and long time scales enables fast as well as precise adaptation of the oscillator for a wide range of frequencies. Amongst others, a very natural implementation of this mechanism is in terms of neural networks. The proposed system enables robotic applications which require fast retuning of locomotion control in order to react to environmental changes or conditions.
    Review:

    © 2011 - 2017 Dept. of Computational Neuroscience • comments to: sreich _at_ gwdg.de • Impressum / Site Info