Prof. Dr. Poramate Manoonpong

Group(s): Neural Control and Robotics
Email:
poramate.manoonpong@phys.uni-goettingen.de

Global QuickSearch:   Matches: 0

Search Settings

    Author / Editor / Organization
    Year
    Title
    Journal / Proceedings / Book
    Dasgupta, S. and Wörgötter, F. and Manoonpong, P. (2013).
    Information dynamics based self-adaptive reservoir for delay temporal memory tasks. Evolving Systems, 235-249, 4, 4. DOI: 10.1007/s12530-013-9080-y.
    BibTeX:
    @article{dasguptawoergoettermanoonpong2013,
      author = {Dasgupta, S. and Wörgötter, F. and Manoonpong, P.},
      title = {Information dynamics based self-adaptive reservoir for delay temporal memory tasks},
      pages = {235-249},
      journal = {Evolving Systems},
      year = {2013},
      volume= {4},
      number = {4},
      publisher = {Springer Berlin Heidelberg},
      url = {http://dx.doi.org/10.1007/s12530-013-9080-y},
      doi = {10.1007/s12530-013-9080-y},
      abstract = {Recurrent neural networks of the reservoir computing (RC) type have been found useful in various time-series processing tasks with inherent non-linearity and requirements of variable temporal memory. Specifically for delayed response tasks involving the transient memorization of information (temporal memory), self-adaptation in RC is crucial for generalization to varying delays. In this work using information theory, we combine a generalized intrinsic plasticity rule with a local information dynamics based schema of reservoir neuron leak adaptation. This allows the RC network to be optimized in a self-adaptive manner with minimal parameter tuning. Local active information storage, measured as the degree of influence of previous activity on the next time step activity of a neuron, is used to modify its leak-rate. This results in RC network with non-uniform leak rate which depends on the time scales of the incoming input. Intrinsic plasticity (IP) is aimed at maximizing the mutual information between each neurons input and output while maintaining a mean level of activity (homeostasis). Experimental results on two standard benchmark tasks confirm the extended performance of this system as compared to the static RC (fixed leak and no IP) and RC with only IP. In addition, using both a simulated wheeled robot and a more complex physical hexapod robot, we demonstrate the ability of the system to achieve long temporal memory for solving a basic T-shaped maze navigation task with varying delay time scale.}}
    Abstract: Recurrent neural networks of the reservoir computing (RC) type have been found useful in various time-series processing tasks with inherent non-linearity and requirements of variable temporal memory. Specifically for delayed response tasks involving the transient memorization of information (temporal memory), self-adaptation in RC is crucial for generalization to varying delays. In this work using information theory, we combine a generalized intrinsic plasticity rule with a local information dynamics based schema of reservoir neuron leak adaptation. This allows the RC network to be optimized in a self-adaptive manner with minimal parameter tuning. Local active information storage, measured as the degree of influence of previous activity on the next time step activity of a neuron, is used to modify its leak-rate. This results in RC network with non-uniform leak rate which depends on the time scales of the incoming input. Intrinsic plasticity (IP) is aimed at maximizing the mutual information between each neurons input and output while maintaining a mean level of activity (homeostasis). Experimental results on two standard benchmark tasks confirm the extended performance of this system as compared to the static RC (fixed leak and no IP) and RC with only IP. In addition, using both a simulated wheeled robot and a more complex physical hexapod robot, we demonstrate the ability of the system to achieve long temporal memory for solving a basic T-shaped maze navigation task with varying delay time scale.
    Review:
    Kesper, P. and Grinke, E. and Hesse, F. and Wörgötter, F. and Manoonpong, P. (2013).
    Obstacle/Gap Detection and Terrain Classification of Walking Robots based on a 2D Laser Range Finder. 16th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines CLAWAR, 419-426, 16.
    BibTeX:
    @inproceedings{kespergrinkehesse2013,
      author = {Kesper, P. and Grinke, E. and Hesse, F. and Wörgötter, F. and Manoonpong, P.},
      title = {Obstacle/Gap Detection and Terrain Classification of Walking Robots based on a 2D Laser Range Finder},
      pages = {419-426},
      booktitle = {16th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines CLAWAR},
      year = {2013},
      number = {16},
      location = {Sidney (Australia)},
      month = {July 14-17},
      abstract = {This paper utilizes a 2D laser range finder (LRF) to determine the behavior of a walking robot. The LRF provides information for 1) obstacle/gap detection as well as 2) terrain classification. The obstacle/gap detection is based on an edge detection with increased robustness and accuracy due to customized pre and post processing. Its output is used to drive obstacle/gap avoidance behavior or climbing behavior, depending on the height of obstacles or the depth of gaps. The terrain classification employs terrain roughness to select a proper gait with respect to the current terrain. As a result, the combination of these methods enables the robot to decide if obstacles and gaps can be climbed up/down or have to be avoided while at the same time a terrain specific gait can be chosen.}}
    Abstract: This paper utilizes a 2D laser range finder (LRF) to determine the behavior of a walking robot. The LRF provides information for 1) obstacle/gap detection as well as 2) terrain classification. The obstacle/gap detection is based on an edge detection with increased robustness and accuracy due to customized pre and post processing. Its output is used to drive obstacle/gap avoidance behavior or climbing behavior, depending on the height of obstacles or the depth of gaps. The terrain classification employs terrain roughness to select a proper gait with respect to the current terrain. As a result, the combination of these methods enables the robot to decide if obstacles and gaps can be climbed up/down or have to be avoided while at the same time a terrain specific gait can be chosen.
    Review:
    Manoonpong, P. and Kolodziejski, C. and Wörgötter, F. and Morimoto, J. (2013).
    Combining Correlation-Based and Reward-Based Learning in Neural Control for Policy Improvement. Advances in Complex Systems, 1350015, 16, 2-3. DOI: 10.1142/S021952591350015X.
    BibTeX:
    @article{manoonpongkolodziejskiwoergoetter20,
      author = {Manoonpong, P. and Kolodziejski, C. and Wörgötter, F. and Morimoto, J.},
      title = {Combining Correlation-Based and Reward-Based Learning in Neural Control for Policy Improvement},
      pages = {1350015},
      journal = {Advances in Complex Systems},
      year = {2013},
      volume= {16},
      number = {2-3},
      url = {http://www.worldscientific.com/doi/abs/10.1142/S021952591350015X},
      doi = {10.1142/S021952591350015X},
      abstract = {Classical conditioning (conventionally modeled as correlation-based learning) and operant conditioning (conventionally modeled as reinforcement learning or reward-based learning) have been found in biological systems. Evidence shows that these two mechanisms strongly involve learning about associations. Based on these biological findings, we propose a new learning model to achieve successful control policies for artificial systems. This model combines correlation-based learning using input correlation learning (ICO learning) and reward-based learning using continuous actor-critic reinforcement learning (RL), thereby working as a dual learner system. The model performance is evaluated by simulations of a cart-pole system as a dynamic motion control problem and a mobile robot system as a goal-directed behavior control problem. Results show that the model can strongly improve pole balancing control policy, i.e., it allows the controller to learn stabilizing the pole in the largest domain of initial conditions compared to the results obtained when using a single learning mechanism. This model can also find a successful control policy for goal-directed behavior, i.e., the robot can effectively learn to approach a given goal compared to its individual components. Thus, the study pursued here sharpens our understanding of how two different learning mechanisms can be combined and complement each other for solving complex tasks.}}
    Abstract: Classical conditioning (conventionally modeled as correlation-based learning) and operant conditioning (conventionally modeled as reinforcement learning or reward-based learning) have been found in biological systems. Evidence shows that these two mechanisms strongly involve learning about associations. Based on these biological findings, we propose a new learning model to achieve successful control policies for artificial systems. This model combines correlation-based learning using input correlation learning (ICO learning) and reward-based learning using continuous actor-critic reinforcement learning (RL), thereby working as a dual learner system. The model performance is evaluated by simulations of a cart-pole system as a dynamic motion control problem and a mobile robot system as a goal-directed behavior control problem. Results show that the model can strongly improve pole balancing control policy, i.e., it allows the controller to learn stabilizing the pole in the largest domain of initial conditions compared to the results obtained when using a single learning mechanism. This model can also find a successful control policy for goal-directed behavior, i.e., the robot can effectively learn to approach a given goal compared to its individual components. Thus, the study pursued here sharpens our understanding of how two different learning mechanisms can be combined and complement each other for solving complex tasks.
    Review:
    Manoonpong, P. and Parlitz, U. and Wörgötter, F. (2013).
    Neural Control and Adaptive Neural Forward Models for Insect-like, Energy-Efficient, and Adaptable Locomotion of Walking Machines. Frontiers in Neural Circuits, 7, 12. DOI: 10.3389/fncir.2013.00012.
    BibTeX:
    @article{manoonpongparlitzwoergoetter2013,
      author = {Manoonpong, P. and Parlitz, U. and Wörgötter, F.},
      title = {Neural Control and Adaptive Neural Forward Models for Insect-like, Energy-Efficient, and Adaptable Locomotion of Walking Machines},
      journal = {Frontiers in Neural Circuits},
      year = {2013},
      volume= {7},
      number = {12},
      url = {http://journal.frontiersin.org/Journal/10.3389/fncir.2013.00012/full},
      doi = {10.3389/fncir.2013.00012},
      abstract = {Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.}}
    Abstract: Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.
    Review:
    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:
    Zenker, S. and Aksoy, E E. and Goldschmidt, D. and Wörgötter, F. and Manoonpong, P. (2013).
    Visual Terrain Classification for Selecting Energy Efficient Gaits of a Hexapod Robot. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 577-584. DOI: 10.1109/AIM.2013.6584154.
    BibTeX:
    @inproceedings{zenkeraksoygoldschmidt2013,
      author = {Zenker, S. and Aksoy, E E. and Goldschmidt, D. and Wörgötter, F. and Manoonpong, P.},
      title = {Visual Terrain Classification for Selecting Energy Efficient Gaits of a Hexapod Robot},
      pages = {577-584},
      booktitle = {IEEE/ASME International Conference on Advanced Intelligent Mechatronics},
      year = {2013},
      location = {Wollongong (Australia)},
      month = {Jul 9-12},
      doi = {10.1109/AIM.2013.6584154},
      abstract = {Legged robots need to be able to classify and recognize different terrains to adapt their gait accordingly. Recent works in terrain classification use different types of sensors (like stereovision, 3D laser range, and tactile sensors) and their combination. However, such sensor systems require more computing power, produce extra load to legged robots, and/or might be difficult to install on a small size legged robot. In this work, we present an online terrain classification system. It uses only a monocular camera with a feature-based terrain classification algorithm which is robust to changes in illumination and view points. For this algorithm, we extract local features of terrains using either Scale Invariant Feature Transform (SIFT) or Speed Up Robust Feature (SURF). We encode the features using the Bag of Words (BoW) technique, and then classify the words using Support Vector Machines (SVMs) with a radial basis function kernel. We compare this feature-based approach with a color-based approach on the Caltech-256 benchmark as well as eight different terrain image sets (grass, gravel, pavement, sand, asphalt, floor, mud, and fine gravel). For terrain images, we observe up to 90% accuracy with the feature-based approach. Finally, this online terrain classification system is successfully applied to our small hexapod robot AMOS II. The output of the system providing terrain information is used as an input to its neural locomotion control to trigger an energy-efficient gait while traversing different terrains.}}
    Abstract: Legged robots need to be able to classify and recognize different terrains to adapt their gait accordingly. Recent works in terrain classification use different types of sensors (like stereovision, 3D laser range, and tactile sensors) and their combination. However, such sensor systems require more computing power, produce extra load to legged robots, and/or might be difficult to install on a small size legged robot. In this work, we present an online terrain classification system. It uses only a monocular camera with a feature-based terrain classification algorithm which is robust to changes in illumination and view points. For this algorithm, we extract local features of terrains using either Scale Invariant Feature Transform (SIFT) or Speed Up Robust Feature (SURF). We encode the features using the Bag of Words (BoW) technique, and then classify the words using Support Vector Machines (SVMs) with a radial basis function kernel. We compare this feature-based approach with a color-based approach on the Caltech-256 benchmark as well as eight different terrain image sets (grass, gravel, pavement, sand, asphalt, floor, mud, and fine gravel). For terrain images, we observe up to 90% accuracy with the feature-based approach. Finally, this online terrain classification system is successfully applied to our small hexapod robot AMOS II. The output of the system providing terrain information is used as an input to its neural locomotion control to trigger an energy-efficient gait while traversing different terrains.
    Review:
    Xiong, X. and Wörgötter, F. and Manoonpong, P. (2013).
    A Simplified Variable Admittance Controller Based on a Virtual Agonist-Antagonist Mechanism for Robot Joint Control. Proc. Intl Conf. on Climbing and Walking Robots CLAWAR 2013, 281-288.
    BibTeX:
    @inproceedings{xiongwoergoettermanoonpong2013a,
      author = {Xiong, X. and Wörgötter, F. and Manoonpong, P.},
      title = {A Simplified Variable Admittance Controller Based on a Virtual Agonist-Antagonist Mechanism for Robot Joint Control},
      pages = {281-288},
      booktitle = {Proc. Intl Conf. on Climbing and Walking Robots CLAWAR 2013},
      year = {2013},
      month = {July},
      abstract = {Physiological studies suggest that the integration of neural circuits and biomechanics (e.g., muscles) is a key for animals to achieve robust and efficient locomotion over challenging surfaces. Inspired by these studies, we present a neuromechanical controller of a hexapod robot for walking on soft elastic and loose surfaces. It consists of a modular neural network (MNN) and virtual agonist-antagonist mechanisms (VAAM, i.e., a muscle model). The MNN coordinates 18 joints and generates basic locomotion while variable joint compliance for walking on different surfaces is achieved by the VAAM. The changeable compliance of each joint does not depend on physical compliant mechanisms or joint torque sensing. Instead, the compliance is altered by two internal parameters of the VAAM. The performance of the controller is tested on a physical hexapod robot for walking on soft elastic (e.g., sponge) and loose (e.g., gravel and snow) surfaces. The experimental results show that the controller enables the hexapod robot to achieve variably compliant leg behaviors, thereby leading to more energy-efficient locomotion on different surfaces. In addition, a finding of the experiments complies with the finding of physiological experiments on cockroach locomotion on soft elastic surfaces,}}
    Abstract: Physiological studies suggest that the integration of neural circuits and biomechanics (e.g., muscles) is a key for animals to achieve robust and efficient locomotion over challenging surfaces. Inspired by these studies, we present a neuromechanical controller of a hexapod robot for walking on soft elastic and loose surfaces. It consists of a modular neural network (MNN) and virtual agonist-antagonist mechanisms (VAAM, i.e., a muscle model). The MNN coordinates 18 joints and generates basic locomotion while variable joint compliance for walking on different surfaces is achieved by the VAAM. The changeable compliance of each joint does not depend on physical compliant mechanisms or joint torque sensing. Instead, the compliance is altered by two internal parameters of the VAAM. The performance of the controller is tested on a physical hexapod robot for walking on soft elastic (e.g., sponge) and loose (e.g., gravel and snow) surfaces. The experimental results show that the controller enables the hexapod robot to achieve variably compliant leg behaviors, thereby leading to more energy-efficient locomotion on different surfaces. In addition, a finding of the experiments complies with the finding of physiological experiments on cockroach locomotion on soft elastic surfaces,
    Review:
    Xiong, X. and Wörgötter, F. and Manoonpong, P. (2013).
    A Neuromechanical Controller of a Hexapod Robot for Walking on Sponge, Gravel and Snow Surfaces. Advances in Artificial Life. Proceedings of the 11th European Conference on Artificial Life ECAL, 989-996.
    BibTeX:
    @inproceedings{xiongwoergoettermanoonpong2013,
      author = {Xiong, X. and Wörgötter, F. and Manoonpong, P.},
      title = {A Neuromechanical Controller of a Hexapod Robot for Walking on Sponge, Gravel and Snow Surfaces},
      pages = {989-996},
      booktitle = {Advances in Artificial Life. Proceedings of the 11th European Conference on Artificial Life ECAL},
      year = {2013},
      editor = {Pietro Lio, Orazio Miglino, Giuseppe Nicosia, Stefano Nolfi and Mario Pavone},
      location = {Taormina (Italy)},
      month = {September 2-6},
      publisher = {MIT Press, Cambridge, MA},
      abstract = {Physiological studies suggest that the integration of neural circuits and biomechanics (e.g., muscles) is a key for animals to achieve robust and efficient locomotion over challenging surfaces. Inspired by these studies, we present a neuromechanical controller of a hexapod robot for walking on soft elastic and loose surfaces. It consists of a modular neural network (MNN) and virtual agonist-antagonist mechanisms (VAAM, i.e., a muscle model). The MNN coordinates 18 joints and generates basic locomotion while variable joint compliance for walking on different surfaces is achieved by the VAAM. The changeable compliance of each joint does not depend on physical compliant mechanisms or joint torque sensing. Instead, the compliance is altered by two internal parameters of the VAAM. The performance of the controller is tested on a physical hexapod robot for walking on soft elastic (e.g., sponge) and loose (e.g., gravel and snow) surfaces. The experimental results show that the controller enables the hexapod robot to achieve variably compliant leg behaviors, thereby leading to more energy-efficient locomotion on different surfaces. In addition, a finding of the experiments complies with the finding of physiological experiments on cockroach locomotion on soft elastic surfaces.}}
    Abstract: Physiological studies suggest that the integration of neural circuits and biomechanics (e.g., muscles) is a key for animals to achieve robust and efficient locomotion over challenging surfaces. Inspired by these studies, we present a neuromechanical controller of a hexapod robot for walking on soft elastic and loose surfaces. It consists of a modular neural network (MNN) and virtual agonist-antagonist mechanisms (VAAM, i.e., a muscle model). The MNN coordinates 18 joints and generates basic locomotion while variable joint compliance for walking on different surfaces is achieved by the VAAM. The changeable compliance of each joint does not depend on physical compliant mechanisms or joint torque sensing. Instead, the compliance is altered by two internal parameters of the VAAM. The performance of the controller is tested on a physical hexapod robot for walking on soft elastic (e.g., sponge) and loose (e.g., gravel and snow) surfaces. The experimental results show that the controller enables the hexapod robot to achieve variably compliant leg behaviors, thereby leading to more energy-efficient locomotion on different surfaces. In addition, a finding of the experiments complies with the finding of physiological experiments on cockroach locomotion on soft elastic surfaces.
    Review:
    Dasgupta, S. and Wörgötter, F. and Morimoto, J. and Manoonpong, P. (2013).
    Neural Combinatorial Learning of Goal-directed Behavior with Reservoir Critic and Reward Modulated Hebbian Plasticity. IEEE International Conference on Systems, Man, and Cybernetics SMC, 993-1000. DOI: 10.1109/SMC.2013.174.
    BibTeX:
    @inproceedings{dasguptawoergoettermorimoto2013,
      author = {Dasgupta, S. and Wörgötter, F. and Morimoto, J. and Manoonpong, P.},
      title = {Neural Combinatorial Learning of Goal-directed Behavior with Reservoir Critic and Reward Modulated Hebbian Plasticity},
      pages = {993-1000},
      booktitle = {IEEE International Conference on Systems, Man, and Cybernetics SMC},
      year = {2013},
      location = {Manchester (UK)},
      month = {October 13-16},
      doi = {10.1109/SMC.2013.174},
      abstract = {Learning of goal-directed behaviors in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). Although traditionally modeled as separate learning systems in artificial agents, numerous animal experiments point towards their co-operative role in behavioral learning. Based on this concept, the recently introduced framework of neural combinatorial learning combines the two systems where both the systems run in parallel to guide the overall learned behavior. Such a combinatorial learning demonstrates a faster and efficient learner. In this work, we further improve the framework by applying a reservoir computing network (RC) as an adaptive critic unit and reward modulated Hebbian plasticity. Using a mobile robot system for goal-directed behavior learning, we clearly demonstrate that the reservoir critic outperforms traditional radial basis function (RBF) critics in terms of stability of convergence and learning time. Furthermore the temporal memory in RC allows the system to learn partially observable markov decision process scenario, in contrast to a memory less RBF critic.}}
    Abstract: Learning of goal-directed behaviors in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). Although traditionally modeled as separate learning systems in artificial agents, numerous animal experiments point towards their co-operative role in behavioral learning. Based on this concept, the recently introduced framework of neural combinatorial learning combines the two systems where both the systems run in parallel to guide the overall learned behavior. Such a combinatorial learning demonstrates a faster and efficient learner. In this work, we further improve the framework by applying a reservoir computing network (RC) as an adaptive critic unit and reward modulated Hebbian plasticity. Using a mobile robot system for goal-directed behavior learning, we clearly demonstrate that the reservoir critic outperforms traditional radial basis function (RBF) critics in terms of stability of convergence and learning time. Furthermore the temporal memory in RC allows the system to learn partially observable markov decision process scenario, in contrast to a memory less RBF critic.
    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:
    Xiong, X. and Wörgötter, F. and Manoonpong, P. (2012).
    An Adaptive Neuromechanical Model for Muscle Impedance Modulations of Legged Robots. International Conference on Dynamic Walking 2012, 1-3.
    BibTeX:
    @conference{xiongwoergoettermanoonpong2012,
      author = {Xiong, X. and Wörgötter, F. and Manoonpong, P.},
      title = {An Adaptive Neuromechanical Model for Muscle Impedance Modulations of Legged Robots},
      pages = {1-3},
      booktitle = {International Conference on Dynamic Walking 2012},
      year = {2012},
      month = {05},
      url = {http://www.bccn-goettingen.de/Publications/articlereference.2012-06-13.4632442521},
      abstract = {Recently, an integrative view of neural circuits and mechanical components has been developed by neuroscientists and biomechanicians 11, 8. This view argues that mechanical components cannot be isolated from neural circuits in the context of substantially perturbed locomotion. Note that mechanical passive walkers with no neural circuits only show stable locomotion on flat terrain or small slopes 2. The argument of the integrative view has been supported by a cockroach experiment, which has demonstrated that more modulations of neural activities are detected when cockroaches run over a highly complex terrain with larger obstacles (more than three times cockroach hip height). Normally, cockroaches are able to solely rely on passive mechanical properties for rapid stabilization while confronted with moderate obstacles (less than three times cockroach hip height) 10. In addition, neural circuits and leg muscle activities tend to be entrained by mechanical feedback 11, 12, 14. Besides, it is well known that neural activities modulate muscle impedance such as stiffness and damping 7, 9, 15, such modulations can be utilized for stabilization in posture and locomotion 3.}}
    Abstract: Recently, an integrative view of neural circuits and mechanical components has been developed by neuroscientists and biomechanicians 11, 8. This view argues that mechanical components cannot be isolated from neural circuits in the context of substantially perturbed locomotion. Note that mechanical passive walkers with no neural circuits only show stable locomotion on flat terrain or small slopes 2. The argument of the integrative view has been supported by a cockroach experiment, which has demonstrated that more modulations of neural activities are detected when cockroaches run over a highly complex terrain with larger obstacles (more than three times cockroach hip height). Normally, cockroaches are able to solely rely on passive mechanical properties for rapid stabilization while confronted with moderate obstacles (less than three times cockroach hip height) 10. In addition, neural circuits and leg muscle activities tend to be entrained by mechanical feedback 11, 12, 14. Besides, it is well known that neural activities modulate muscle impedance such as stiffness and damping 7, 9, 15, such modulations can be utilized for stabilization in posture and locomotion 3.
    Review:
    Goldschmidt, D. and Hesse, F. and Wörgötter, F. and Manoonpong, P. (2012).
    Biologically inspired reactive climbing behavior of hexapod robots. IEEE/RSJ International Conference on Intelligent Robots and Systems IROS, 4632-4637. DOI: 10.1109/IROS.2012.6386135.
    BibTeX:
    @inproceedings{goldschmidthessewoergoetter2012,
      author = {Goldschmidt, D. and Hesse, F. and Wörgötter, F. and Manoonpong, P.},
      title = {Biologically inspired reactive climbing behavior of hexapod robots},
      pages = {4632-4637},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems IROS},
      year = {2012},
      doi = {10.1109/IROS.2012.6386135},
      abstract = {Insects, e.g. cockroaches and stick insects, have found fascinating solutions for the problem of locomotion, especially climbing over a large variety of obstacles. Research on behavioral neurobiology has identified key behavioral patterns of these animals (i.e., body flexion, center of mass elevation, and local leg reflexes) necessary for climbing. Inspired by this finding, we develop a neural control mechanism for hexapod robots which generates basic walking behavior and especially enables them to effectively perform reactive climbing behavior. The mechanism is composed of three main neural circuits: locomotion control, reactive backbone joint control, and local leg reflex control. It was developed and tested using a physical simulation environment, and was then successfully transferred to a physical six-legged walking machine, called AMOS II. Experimental results show that the controller allows the robot to overcome obstacles of various heights (e.g., 75% of its leg length, which are higher than those that other comparable legged robots have achieved so far). The generated climbing behavior is also comparable to the one observed in cockroaches.}}
    Abstract: Insects, e.g. cockroaches and stick insects, have found fascinating solutions for the problem of locomotion, especially climbing over a large variety of obstacles. Research on behavioral neurobiology has identified key behavioral patterns of these animals (i.e., body flexion, center of mass elevation, and local leg reflexes) necessary for climbing. Inspired by this finding, we develop a neural control mechanism for hexapod robots which generates basic walking behavior and especially enables them to effectively perform reactive climbing behavior. The mechanism is composed of three main neural circuits: locomotion control, reactive backbone joint control, and local leg reflex control. It was developed and tested using a physical simulation environment, and was then successfully transferred to a physical six-legged walking machine, called AMOS II. Experimental results show that the controller allows the robot to overcome obstacles of various heights (e.g., 75% of its leg length, which are higher than those that other comparable legged robots have achieved so far). The generated climbing behavior is also comparable to the one observed in cockroaches.
    Review:
    Dasgupta, S. and Wörgötter, F. and Manoonpong, P. (2012).
    Information Theoretic Self-organised Adaptation in Reservoirs for Temporal Memory Tasks. Engineering Applications of Neural Networks, 31-40, 311. DOI: 10.1007/978-3-642-32909-8_4.
    BibTeX:
    @incollection{dasguptawoergoettermanoonpong2012,
      author = {Dasgupta, S. and Wörgötter, F. and Manoonpong, P.},
      title = {Information Theoretic Self-organised Adaptation in Reservoirs for Temporal Memory Tasks},
      pages = {31-40},
      booktitle = {Engineering Applications of Neural Networks},
      year = {2012},
      volume= {311},
      editor = {Jayne, Chrisina and Yue, Shigang and Iliadis, Lazaros},
      publisher = {Springer Berlin Heidelberg},
      series = {Communications},
      url = {http://dx.doi.org/10.1007/978-3-642-32909-8_4},
      doi = {10.1007/978-3-642-32909-8_4},
      abstract = {Recurrent neural networks of the Reservoir Computing (RC) type have been found useful in various time-series processing tasks with inherent non-linearity and requirements of temporal memory. Here with the aim to obtain extended temporal memory in generic delayed response tasks, we combine a generalised intrinsic plasticity mechanism with an information storage based neuron leak adaptation rule in a self-organised manner. This results in adaptation of neuron local memory in terms of leakage along with inherent homeostatic stability. Experimental results on two benchmark tasks confirm the extended performance of this system as compared to a static RC and RC with only intrinsic plasticity. Furthermore, we demonstrate the ability of the system to solve long temporal memory tasks via a simulated T-shaped maze navigation scenario.}}
    Abstract: Recurrent neural networks of the Reservoir Computing (RC) type have been found useful in various time-series processing tasks with inherent non-linearity and requirements of temporal memory. Here with the aim to obtain extended temporal memory in generic delayed response tasks, we combine a generalised intrinsic plasticity mechanism with an information storage based neuron leak adaptation rule in a self-organised manner. This results in adaptation of neuron local memory in terms of leakage along with inherent homeostatic stability. Experimental results on two benchmark tasks confirm the extended performance of this system as compared to a static RC and RC with only intrinsic plasticity. Furthermore, we demonstrate the ability of the system to solve long temporal memory tasks via a simulated T-shaped maze navigation scenario.
    Review:
    Ren, G. and Chen, W. and Kolodziejski, C. and Wörgötter, F. and Dasgupta, S. and Manoonpong, P. (2012).
    Multiple Chaotic Central Pattern Generators for Locomotion Generation and Leg Damage Compensation in a Hexapod Robot. IEEE/RSJ International Conference on Intelligent Robots and Systems IROS. DOI: 10.1109/IROS.2012.6385573.
    BibTeX:
    @inproceedings{renchenkolodziejski2012,
      author = {Ren, G. and Chen, W. and Kolodziejski, C. and Wörgötter, F. and Dasgupta, S. and Manoonpong, P.},
      title = {Multiple Chaotic Central Pattern Generators for Locomotion Generation and Leg Damage Compensation in a Hexapod Robot},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems IROS},
      year = {2012},
      doi = {10.1109/IROS.2012.6385573},
      abstract = {In chaos control, an originally chaotic system is modified so that periodic dynamics arise. One application of this is to use the periodic dynamics of a single chaotic system as walking patterns in legged robots. In our previous work we applied such a controlled chaotic system as a central pattern generator (CPG) to generate different gait patterns of our hexapod robot AMOSII. However, if one or more legs break, its control fails. Specifically, in the scenario presented here, its movement permanently deviates from a desired trajectory. This is in contrast to the movement of real insects as they can compensate for body damages, for instance, by adjusting the remaining legs frequency. To achieve this for our hexapod robot, we extend the system from one chaotic system serving as a single CPG to multiple chaotic systems, performing as multiple CPGs. Without damage, the chaotic systems synchronize and their dynamics is identical (similar to a single CPG). With damage, they can lose synchronization leading to independent dynamics. In both simulations and real experiments, we can tune the oscillation frequency of every CPG manually so that the controller can indeed compensate for leg damage. In comparison to the trajectory of the robot controlled by only a single CPG, the trajectory produced by multiple chaotic CPG controllers resembles the original trajectory by far better. Thus, multiple chaotic systems that synchronize for normal behavior but can stay desynchronized in other circumstances are an effective way to control complex behaviors where, for instance, different body parts have to do independent movements like after leg damage.}}
    Abstract: In chaos control, an originally chaotic system is modified so that periodic dynamics arise. One application of this is to use the periodic dynamics of a single chaotic system as walking patterns in legged robots. In our previous work we applied such a controlled chaotic system as a central pattern generator (CPG) to generate different gait patterns of our hexapod robot AMOSII. However, if one or more legs break, its control fails. Specifically, in the scenario presented here, its movement permanently deviates from a desired trajectory. This is in contrast to the movement of real insects as they can compensate for body damages, for instance, by adjusting the remaining legs frequency. To achieve this for our hexapod robot, we extend the system from one chaotic system serving as a single CPG to multiple chaotic systems, performing as multiple CPGs. Without damage, the chaotic systems synchronize and their dynamics is identical (similar to a single CPG). With damage, they can lose synchronization leading to independent dynamics. In both simulations and real experiments, we can tune the oscillation frequency of every CPG manually so that the controller can indeed compensate for leg damage. In comparison to the trajectory of the robot controlled by only a single CPG, the trajectory produced by multiple chaotic CPG controllers resembles the original trajectory by far better. Thus, multiple chaotic systems that synchronize for normal behavior but can stay desynchronized in other circumstances are an effective way to control complex behaviors where, for instance, different body parts have to do independent movements like after leg damage.
    Review:
    Tungpimolrut, K. and Hatti, N. and Phontip, J. and Komoljindakul, K. and Pechrach, K. and Manoonpong, P. (2011).
    Design of Energy Harvester Circuit for a MFC Piezoelectric based on Electrical Circuit Modeling. The 20th International Symposium on Applications of Ferroelectrics and the International Symposium on Piezoresponse Force Microscopy and Nanoscale Phenomena in Polar Materials, 1-4. DOI: 10.1109/ISAF.2011.6014136.
    BibTeX:
    @inproceedings{tungpimolruthattiphontip2011,
      author = {Tungpimolrut, K. and Hatti, N. and Phontip, J. and Komoljindakul, K. and Pechrach, K. and Manoonpong, P.},
      title = {Design of Energy Harvester Circuit for a MFC Piezoelectric based on Electrical Circuit Modeling},
      pages = {1-4},
      booktitle = {The 20th International Symposium on Applications of Ferroelectrics and the International Symposium on Piezoresponse Force Microscopy and Nanoscale Phenomena in Polar Materials},
      year = {2011},
      month = {07},
      doi = {10.1109/ISAF.2011.6014136},
      abstract = {In this paper, the characteristic of the piezoelectric material, Macro Fiber Composites (MFC), has been investigated by comparison between the electrical equivalent circuit based simulation and the experimental result. The operational factors such as internal impedance and frequency which affect the maximum power output of the piezoelectric are systematically determined. The effect from the characteristic of the capacity after the rectifier circuit of the energy harvesting circuit in order to achieve the suitable energy storage is also mentioned. Some basic characteristic are tested and measured based on standard energy harvest kit and commercial MFC.}}
    Abstract: In this paper, the characteristic of the piezoelectric material, Macro Fiber Composites (MFC), has been investigated by comparison between the electrical equivalent circuit based simulation and the experimental result. The operational factors such as internal impedance and frequency which affect the maximum power output of the piezoelectric are systematically determined. The effect from the characteristic of the capacity after the rectifier circuit of the energy harvesting circuit in order to achieve the suitable energy storage is also mentioned. Some basic characteristic are tested and measured based on standard energy harvest kit and commercial MFC.
    Review:
    Pechrach, K. and Manoonpong, P. and Wörgötter, F. and Tungpimolrut, K. and Hatti, N. and Phontip, J. and Komoljindakul, K. (2011).
    Piezoelectric Energy Harvesting for Self Power Generation of Upper and Lower Prosthetic Legs. International Conference on Piezo 2011-Electroceramics for End-Users VI.
    BibTeX:
    @inproceedings{pechrachmanoonpongwoergoetter2011,
      author = {Pechrach, K. and Manoonpong, P. and Wörgötter, F. and Tungpimolrut, K. and Hatti, N. and Phontip, J. and Komoljindakul, K.},
      title = {Piezoelectric Energy Harvesting for Self Power Generation of Upper and Lower Prosthetic Legs},
      booktitle = {International Conference on Piezo 2011-Electroceramics for End-Users VI},
      year = {2011},
      abstract = {This works present the design of an energy harvesting system using smart materials for self power generation of upper and lower prosthetic legs. The smart materials like Piezo-Composites, Piezo Flexible Film, Macro Fiber Composites, and PZT have been employed and modified to be appropriately embedded in the prosthesis. The movements of the prosthesis would extract and transfer energy directly from the piezoelectric via a converter to a power management system. Afterward, the power management system manages and accumulates the generated electrical energy to be sufficient for later powering electronic components of the prosthesis. Here we show our preliminary experimental results of energy harvesting and efficiency in peak piezoelectric voltages during step up and continuous walking for a period of time.}}
    Abstract: This works present the design of an energy harvesting system using smart materials for self power generation of upper and lower prosthetic legs. The smart materials like Piezo-Composites, Piezo Flexible Film, Macro Fiber Composites, and PZT have been employed and modified to be appropriately embedded in the prosthesis. The movements of the prosthesis would extract and transfer energy directly from the piezoelectric via a converter to a power management system. Afterward, the power management system manages and accumulates the generated electrical energy to be sufficient for later powering electronic components of the prosthesis. Here we show our preliminary experimental results of energy harvesting and efficiency in peak piezoelectric voltages during step up and continuous walking for a period of time.
    Review:
    Manoonpong, P. and Wörgötter, F. and Pechrach, K. and Tungpimolrut, K. and Hatti, N. and Phontip, J. and Komol, K. (2011).
    Using Neural Networks for Modelling Piezoelectric Energy Harvesting Systems in a Prosthetic Leg. International Conference on Piezo 2011-Electroceramics for End-Users VI.
    BibTeX:
    @inproceedings{manoonpongwoergoetterpechrach2011,
      author = {Manoonpong, P. and Wörgötter, F. and Pechrach, K. and Tungpimolrut, K. and Hatti, N. and Phontip, J. and Komol, K.},
      title = {Using Neural Networks for Modelling Piezoelectric Energy Harvesting Systems in a Prosthetic Leg},
      booktitle = {International Conference on Piezo 2011-Electroceramics for End-Users VI},
      year = {2011},
      abstract = {In this paper, we present energy harvesting systems in a prosthetic leg using piezoceramic Macro Fiber Composites MFCs and their models using artificial neural networks. The piezoceramic MFCs are implemented at the sole and heel of the leg and transform impact forces into electrical power during walking. The neural model of the energy harvesting system installed at the sole is developed on the basis of a standard feedforward backpropagation neural network. On the other hand, the neural model of the energy harvesting system installed at the heel is manually synthesized from different neural modules networks. Experimental results show that these neural models can appropriately transform the impact forces detected by force sensing resistors FSRs into the electrical responses of the piezoceramic MFCs. The models will be used to study and analyze dynamical behaviors of the piezoelectric materials with respect to walking}}
    Abstract: In this paper, we present energy harvesting systems in a prosthetic leg using piezoceramic Macro Fiber Composites MFCs and their models using artificial neural networks. The piezoceramic MFCs are implemented at the sole and heel of the leg and transform impact forces into electrical power during walking. The neural model of the energy harvesting system installed at the sole is developed on the basis of a standard feedforward backpropagation neural network. On the other hand, the neural model of the energy harvesting system installed at the heel is manually synthesized from different neural modules networks. Experimental results show that these neural models can appropriately transform the impact forces detected by force sensing resistors FSRs into the electrical responses of the piezoceramic MFCs. The models will be used to study and analyze dynamical behaviors of the piezoelectric materials with respect to walking
    Review:
    Manoonpong, P. and Wörgötter, F. and Pasemann, F. (2011).
    Biological Inspiration for Mechanical Design and Control of Autonomous Walking Robots: Towards Life-Like Robots. Int. J. Appl. Biomed. Eng. IJABME, 1-12, 31.
    BibTeX:
    @article{manoonpongwoergoetterpasemann2011,
      author = {Manoonpong, P. and Wörgötter, F. and Pasemann, F.},
      title = {Biological Inspiration for Mechanical Design and Control of Autonomous Walking Robots: Towards Life-Like Robots},
      pages = {1-12},
      journal = {Int. J. Appl. Biomed. Eng. IJABME},
      year = {2011},
      volume= {31},
      abstract = {Nature apparently has succeeded in evolving biomechanics and creating neural mechanisms that allow living systems like walking animals to perform various sophisticated behaviors, e.g. and different gaits, climbing, turning, orienting, obstacle avoidance, attraction, anticipation. This shows that general principles of nature can provide biological inspiration for robotic designs or give useful hints of what is possible and design ideas that may have escaped our consideration. Instead of starting from scratch, this article presents how the biological principles can be used for mechanical design and control of walking robots, in order to approach living creatures in their level of performance. Employing this strategy allows us to successfully develop versatile, adaptive, and autonomous walking robots. Versatility in this sense means a variety of reactive behaviors including memory guidance, while adaptivity implies online learning capabilities. Autonomy is an ability to function without continuous human guidance. These three key elements are achieved under modular neural control and learning. In addition, the presented neural control technique is shown to be a powerful method of solving sensor-motor coordination problems of high complexity systems}}
    Abstract: Nature apparently has succeeded in evolving biomechanics and creating neural mechanisms that allow living systems like walking animals to perform various sophisticated behaviors, e.g. and different gaits, climbing, turning, orienting, obstacle avoidance, attraction, anticipation. This shows that general principles of nature can provide biological inspiration for robotic designs or give useful hints of what is possible and design ideas that may have escaped our consideration. Instead of starting from scratch, this article presents how the biological principles can be used for mechanical design and control of walking robots, in order to approach living creatures in their level of performance. Employing this strategy allows us to successfully develop versatile, adaptive, and autonomous walking robots. Versatility in this sense means a variety of reactive behaviors including memory guidance, while adaptivity implies online learning capabilities. Autonomy is an ability to function without continuous human guidance. These three key elements are achieved under modular neural control and learning. In addition, the presented neural control technique is shown to be a powerful method of solving sensor-motor coordination problems of high complexity systems
    Review:
    Manoonpong, P. and Kulvicius, T. and Wörgötter, F. and Kunze, L. and Renjewski, D. and Seyfarth, A. (2011).
    Compliant Ankles and Flat Feet for Improved Self-Stabilization and Passive Dynamics of the Biped Robot RunBot. The 2011 IEEE-RAS International Conference on Humanoid Robots, 276 - 281. DOI: 10.1109/Humanoids.2011.6100804.
    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},
      pages = {276 - 281},
      booktitle = {The 2011 IEEE-RAS International Conference on Humanoid Robots},
      year = {2011},
      month = {10},
      doi = {10.1109/Humanoids.2011.6100804},
      abstract = {Biomechanical studies of human walking reveal that compliance plays an important role at least in natural and smooth motions as well as for self-stabilization. Inspired by this, we present here the development of a new lower leg segment of the dynamic biped robot "RunBot". This new lower leg segment features a compliant ankle connected to a flat foot. It is mainly employed to realize robust self-stabilization in a passive manner. In general, such self-stabilization is achieved through mechanical feedback due to elasticity. Using real-time walking experiments, this study shows that the new lower leg segment improves dynamic walking behavior of the robot in two main respects compared to an old lower leg segment consisting of rigid ankle and curved foot: 1) it provides better self-stabilization after stumbling and 2) it increases passive dynamics during some stages of the gait cycle of the robot i.e., when the whole robot moves unactuated. As a consequence, a combination of compliance (i.e., the new lower leg segment) and active components (i.e., actuated hip and knee joints) driven by a neural mechanism (i.e., reflexive neural control) enables RunBot to perform robust self stabilization and at the same time natural, smooth, and energy efficient walking behavior without high control effort.}}
    Abstract: Biomechanical studies of human walking reveal that compliance plays an important role at least in natural and smooth motions as well as for self-stabilization. Inspired by this, we present here the development of a new lower leg segment of the dynamic biped robot "RunBot". This new lower leg segment features a compliant ankle connected to a flat foot. It is mainly employed to realize robust self-stabilization in a passive manner. In general, such self-stabilization is achieved through mechanical feedback due to elasticity. Using real-time walking experiments, this study shows that the new lower leg segment improves dynamic walking behavior of the robot in two main respects compared to an old lower leg segment consisting of rigid ankle and curved foot: 1) it provides better self-stabilization after stumbling and 2) it increases passive dynamics during some stages of the gait cycle of the robot i.e., when the whole robot moves unactuated. As a consequence, a combination of compliance (i.e., the new lower leg segment) and active components (i.e., actuated hip and knee joints) driven by a neural mechanism (i.e., reflexive neural control) enables RunBot to perform robust self stabilization and at the same time natural, smooth, and energy efficient walking behavior without high control effort.
    Review:
    Hesse, F. and Manoonpong, P. and Wörgötter, F. (2011).
    A Neural Pre- and Post-Processing Framework for Goal Directed Behavior in Self-Organizing Robots. The 21st Annual Conference of the Japanese Neural Network Society.
    BibTeX:
    @inproceedings{hessemanoonpongwoergoetter2011,
      author = {Hesse, F. and Manoonpong, P. and Wörgötter, F.},
      title = {A Neural Pre- and Post-Processing Framework for Goal Directed Behavior in Self-Organizing Robots},
      booktitle = {The 21st Annual Conference of the Japanese Neural Network Society},
      year = {2011},
      abstract = {In the work at hand we introduce a neural pre- and post-processing framework whose parameters can be adapted by any learning mechanism, e.g. reinforcement learning. The framework allows to generate goal-directed behaviors while at the same time exploiting the bene cial properties, e.g. robustness, of self-organization based primitive behaviors}}
    Abstract: In the work at hand we introduce a neural pre- and post-processing framework whose parameters can be adapted by any learning mechanism, e.g. reinforcement learning. The framework allows to generate goal-directed behaviors while at the same time exploiting the bene cial properties, e.g. robustness, of self-organization based primitive behaviors
    Review:
    Hatti, N. and Tungpimolrut, K. and Phontip, J. and Pechrach, K. and Manoonpong, P. and Komol, K. (2011).
    A PZT Modeling for Energy Harvesting Circuits. The 20th International Symposium on Applications of Ferroelectrics and the International Symposium on Piezoresponse Force Microscopy and Nanoscale Phenomena in Polar Materials ISAF-PFM-2011, 23-27. DOI: 10.1109/ISAF.2011.6014127.
    BibTeX:
    @inproceedings{hattitungpimolrutphontip2011,
      author = {Hatti, N. and Tungpimolrut, K. and Phontip, J. and Pechrach, K. and Manoonpong, P. and Komol, K.},
      title = {A PZT Modeling for Energy Harvesting Circuits},
      pages = {23-27},
      booktitle = {The 20th International Symposium on Applications of Ferroelectrics and the International Symposium on Piezoresponse Force Microscopy and Nanoscale Phenomena in Polar Materials ISAF-PFM-2011},
      year = {2011},
      month = {07},
      doi = {10.1109/ISAF.2011.6014127},
      abstract = {This work presents the modeling of PZT (Lead Zirconate Titanate) intended for using in low frequency mechanical movement applications such as prosthetic legs. This includes the simplified PZT electromechanical modeling based on PSCAD/PSPICE, simulation and experiment. The model can emulate the behavior of the PZT in variety conditions. The simulation and experimental results well agree with each other. The benefits of the model are the easiness of analyzing and studying the behavior of PZT when the conditions or applications of use are changed such as in the case of using full-bridge diode rectifier, buck/boost converter, bridgeless rectifier, and series or parallel PZT modules.}}
    Abstract: This work presents the modeling of PZT (Lead Zirconate Titanate) intended for using in low frequency mechanical movement applications such as prosthetic legs. This includes the simplified PZT electromechanical modeling based on PSCAD/PSPICE, simulation and experiment. The model can emulate the behavior of the PZT in variety conditions. The simulation and experimental results well agree with each other. The benefits of the model are the easiness of analyzing and studying the behavior of PZT when the conditions or applications of use are changed such as in the case of using full-bridge diode rectifier, buck/boost converter, bridgeless rectifier, and series or parallel PZT modules.
    Review:
    Chadil, N. and Phadoognsidhi, M. and Suwannasit, K. and Manoonpong, P. and Laksanacharoen, P. (2011).
    A Reconfigurable Spherical Robot. 2011 IEEE International Conference on Robotics and Automation ICRA, Shanghai, China, 2380 - 2385. DOI: 10.1109/ICRA.2011.5979756.
    BibTeX:
    @inproceedings{chadilphadoognsidhisuwannasit2011,
      author = {Chadil, N. and Phadoognsidhi, M. and Suwannasit, K. and Manoonpong, P. and Laksanacharoen, P.},
      title = {A Reconfigurable Spherical Robot},
      pages = {2380 - 2385},
      booktitle = {2011 IEEE International Conference on Robotics and Automation ICRA, Shanghai, China},
      year = {2011},
      month = {05},
      doi = {10.1109/ICRA.2011.5979756},
      abstract = {This paper presents a reconfigurable spherical robot. The reconfigurable spherical robot can be reconfigured into a form of two interconnected hemispheres with three legs equipped with three omni-directional wheels. A stable reconfiguration control algorithm is constructed to change the robot from spherical shape to two halves of interconnected hemispheres and three legged-wheeled expansions. This work also constructs a transformation controller for the robot which uses an accelerometer to sense its orientation. The performance analysis shows that our reconfigurable robot prototype can transform from spherical shape (dormant mode) into two inter connected hemispheres where the three leg-wheels are projected out of the shells (transformed mode) and vice versa. After the transformation into the three leg-wheel configuration, the robot can autonomously move in L-shaped and U-shaped areas as well as narrowing pathways.}}
    Abstract: This paper presents a reconfigurable spherical robot. The reconfigurable spherical robot can be reconfigured into a form of two interconnected hemispheres with three legs equipped with three omni-directional wheels. A stable reconfiguration control algorithm is constructed to change the robot from spherical shape to two halves of interconnected hemispheres and three legged-wheeled expansions. This work also constructs a transformation controller for the robot which uses an accelerometer to sense its orientation. The performance analysis shows that our reconfigurable robot prototype can transform from spherical shape (dormant mode) into two inter connected hemispheres where the three leg-wheels are projected out of the shells (transformed mode) and vice versa. After the transformation into the three leg-wheel configuration, the robot can autonomously move in L-shaped and U-shaped areas as well as narrowing pathways.
    Review:
    Steingrube, S. and Timme, M. and Wörgötter, F. and Manoonpong, P. (2010).
    Self-Organized Adaptation of Simple Neural Circuits Enables Complex Robot Behavior. Nature Physics, 224-230, 6. DOI: 10.1038/nphys1508.
    BibTeX:
    @article{steingrubetimmewoergoetter2010,
      author = {Steingrube, S. and Timme, M. and Wörgötter, F. and Manoonpong, P.},
      title = {Self-Organized Adaptation of Simple Neural Circuits Enables Complex Robot Behavior},
      pages = {224-230},
      journal = {Nature Physics},
      year = {2010},
      volume= {6},
      doi = {10.1038/nphys1508},
      abstract = {Controlling sensori-motor systems in higher animals or complex robots is a challenging combinatorial problem, because many sensory signals need to be simultaneously coordinated into a broad behavioural spectrum. To rapidly interact with the environment, this control needs to be fast and adaptive. Present robotic solutions operate with limited autonomy and are mostly restricted to few behavioural patterns. Here we introduce chaos control as a new strategy to generate complex behaviour of an autonomous robot. In the presented system, 18 sensors drive 18 motors by means of a simple neural control circuit, thereby generating 11 basic behavioural patterns for example, orienting, taxis, self-protection and various gaits and their combinations. The control signal quickly and reversibly adapts to new situations and also enables learning and synaptic long-term storage of behaviourally useful motor responses. Thus, such neural control provides a powerful yet simple way to self-organize versatile behaviours in autonomous agents with many degrees of freedom}}
    Abstract: Controlling sensori-motor systems in higher animals or complex robots is a challenging combinatorial problem, because many sensory signals need to be simultaneously coordinated into a broad behavioural spectrum. To rapidly interact with the environment, this control needs to be fast and adaptive. Present robotic solutions operate with limited autonomy and are mostly restricted to few behavioural patterns. Here we introduce chaos control as a new strategy to generate complex behaviour of an autonomous robot. In the presented system, 18 sensors drive 18 motors by means of a simple neural control circuit, thereby generating 11 basic behavioural patterns for example, orienting, taxis, self-protection and various gaits and their combinations. The control signal quickly and reversibly adapts to new situations and also enables learning and synaptic long-term storage of behaviourally useful motor responses. Thus, such neural control provides a powerful yet simple way to self-organize versatile behaviours in autonomous agents with many degrees of freedom
    Review:
    Schröder-Schetelig, J. and Manoonpong, P. and Wörgötter, F. (2010).
    Using efference copy and a forward internal model for adaptive biped walking. Autonomous Robots, 357-366, 29, 3-4. DOI: 10.1007/s10514-010-9199-7.
    BibTeX:
    @article{schroederscheteligmanoonpongwoergoe,
      author = {Schröder-Schetelig, J. and Manoonpong, P. and Wörgötter, F.},
      title = {Using efference copy and a forward internal model for adaptive biped walking},
      pages = {357-366},
      journal = {Autonomous Robots},
      year = {2010},
      volume= {29},
      number = {3-4},
      language = {English},
      publisher = {Springer US},
      doi = {10.1007/s10514-010-9199-7},
      abstract = {To behave properly in an unknown environment, animals or robots must distinguish external from self-generated stimuli on their sensors. The biologically inspired concepts of efference copy and internal model have been successfully applied to a number of robot control problems. Here we present an application of this for our dynamic walking robot RunBot. We use efference copies of the motor commands with a simple forward internal model to predict the expected self-generated acceleration during walking. The difference to the actually measured acceleration is then used to stabilize the walking on terrains with changing slopes through its up- per body component controller. As a consequence, the con- troller drives the upper body component UBC to lean for- wards/backwards as soon as an error occurs resulting in dy- namical stable walking. We have evaluated the performance of the system on four different track configurations. Further- more we believe that the experimental studies pursued here will sharpen our understanding of how the efference copies influence dynamic locomotion control to the benefit of mod- ern neural control strategies in robots}}
    Abstract: To behave properly in an unknown environment, animals or robots must distinguish external from self-generated stimuli on their sensors. The biologically inspired concepts of efference copy and internal model have been successfully applied to a number of robot control problems. Here we present an application of this for our dynamic walking robot RunBot. We use efference copies of the motor commands with a simple forward internal model to predict the expected self-generated acceleration during walking. The difference to the actually measured acceleration is then used to stabilize the walking on terrains with changing slopes through its up- per body component controller. As a consequence, the con- troller drives the upper body component UBC to lean for- wards/backwards as soon as an error occurs resulting in dy- namical stable walking. We have evaluated the performance of the system on four different track configurations. Further- more we believe that the experimental studies pursued here will sharpen our understanding of how the efference copies influence dynamic locomotion control to the benefit of mod- ern neural control strategies in robots
    Review:
    Manoonpong, P. and Wörgötter, F. and Morimoto, J. (2010).
    Extraction of Reward-Related Feature Space Using Correlation-Based and Reward-Based Learning Methods. ICONIP 1, 414-421, 6443. DOI: 10.1007/978-3-642-17537-4_51.
    BibTeX:
    @inproceedings{manoonpongwoergoettermorimoto2010,
      author = {Manoonpong, P. and Wörgötter, F. and Morimoto, J.},
      title = {Extraction of Reward-Related Feature Space Using Correlation-Based and Reward-Based Learning Methods},
      pages = {414-421},
      booktitle = {ICONIP 1},
      year = {2010},
      volume= {6443},
      doi = {10.1007/978-3-642-17537-4_51},
      abstract = {The purpose of this article is to present a novel learning paradigm that extracts reward-related low-dimensional state space by combining correlation-based learning like Input Correlation Learning ICO learning and reward-based learning like Reinforcement Learn- ing RL. Since ICO learning can quickly find a correlation between a state and an unwanted condition e.g. and failure, we use it to extract low-dimensional feature space in which we can find a failure avoidance policy. Then, the extracted feature space is used as a prior for RL. If we can extract proper feature space for a given task, a model of the policy can be simple and the policy can be easily improved. The performance of this learning paradigm is evaluated through simulation of a cart-pole system. As a result, we show that the proposed method can enhance the feature extraction process to find the proper feature space for a pole bal- ancing policy. That is it allows a policy to effectively stabilize the pole in the largest domain of initial conditions compared to only using ICO learning or only using RL without any prior knowledge}}
    Abstract: The purpose of this article is to present a novel learning paradigm that extracts reward-related low-dimensional state space by combining correlation-based learning like Input Correlation Learning ICO learning and reward-based learning like Reinforcement Learn- ing RL. Since ICO learning can quickly find a correlation between a state and an unwanted condition e.g. and failure, we use it to extract low-dimensional feature space in which we can find a failure avoidance policy. Then, the extracted feature space is used as a prior for RL. If we can extract proper feature space for a given task, a model of the policy can be simple and the policy can be easily improved. The performance of this learning paradigm is evaluated through simulation of a cart-pole system. As a result, we show that the proposed method can enhance the feature extraction process to find the proper feature space for a pole bal- ancing policy. That is it allows a policy to effectively stabilize the pole in the largest domain of initial conditions compared to only using ICO learning or only using RL without any prior knowledge
    Review:
    Manoonpong, P. and Pasemann, F. and Kolodziejski, C. and Wörgötter, F. (2010).
    Designing Simple Nonlinear Filters Using Hysteresis of Single Recurrent Neurons for Acoustic Signal Recognition in Robots. ICANN 1, 374-383, 6352. DOI: 10.1007/978-3-642-15819-3_50.
    BibTeX:
    @inproceedings{manoonpongpasemannkolodziejski2010,
      author = {Manoonpong, P. and Pasemann, F. and Kolodziejski, C. and Wörgötter, F.},
      title = {Designing Simple Nonlinear Filters Using Hysteresis of Single Recurrent Neurons for Acoustic Signal Recognition in Robots},
      pages = {374-383},
      booktitle = {ICANN 1},
      year = {2010},
      volume= {6352},
      doi = {10.1007/978-3-642-15819-3_50},
      abstract = {In this article we exploit the discrete-time dynamics of a sin- gle neuron with self-connection to systematically design simple signal fil- ters. Due to hysteresis effects and transient dynamics, this single neuron behaves as an adjustable low-pass filter for specific parameter configura- tions. Extending this neuro-module by two more recurrent neurons leads to versatile high- and band-pass filters. The approach presented here helps to understand how the dynamical properties of recurrent neural networks can be used for filter design. Furthermore, it gives guidance to a new way of implementing sensory preprocessing for acoustic signal recognition in autonomous robots}}
    Abstract: In this article we exploit the discrete-time dynamics of a sin- gle neuron with self-connection to systematically design simple signal fil- ters. Due to hysteresis effects and transient dynamics, this single neuron behaves as an adjustable low-pass filter for specific parameter configura- tions. Extending this neuro-module by two more recurrent neurons leads to versatile high- and band-pass filters. The approach presented here helps to understand how the dynamical properties of recurrent neural networks can be used for filter design. Furthermore, it gives guidance to a new way of implementing sensory preprocessing for acoustic signal recognition in autonomous robots
    Review:
    Renjewski, D. and Seyfarth, A. and Manoonpong, P. and Wörgötter, F. (2009).
    The development of a biomechanical leg system and its neural control. IEEE International Conference on Robotics and Biomimetics ROBIO, 1894 -1899. DOI: 10.1109/ROBIO.2009.5420535.
    BibTeX:
    @inproceedings{renjewskiseyfarthmanoonpong2009,
      author = {Renjewski, D. and Seyfarth, A. and Manoonpong, P. and Wörgötter, F.},
      title = {The development of a biomechanical leg system and its neural control},
      pages = {1894 -1899},
      booktitle = {IEEE International Conference on Robotics and Biomimetics ROBIO},
      year = {2009},
      doi = {10.1109/ROBIO.2009.5420535},
      abstract = {The function of the locomotor system in human gait is still an open question. Today robot bipeds are not able to reproduce the versatility of human locomotion. In this article a robotic knee joint and an experimental setup are proposed. The leg function is tested and the acquired data is compared to human leg behaviour in running observed in experiments}}
    Abstract: The function of the locomotor system in human gait is still an open question. Today robot bipeds are not able to reproduce the versatility of human locomotion. In this article a robotic knee joint and an experimental setup are proposed. The leg function is tested and the acquired data is compared to human leg behaviour in running observed in experiments
    Review:
    Manoonpong, P. and Wörgötter, F. (2009).
    Efference copies in neural control of dynamic biped walking. Robotics and Autonomous Systems, 1140-1153, 57, 11.
    BibTeX:
    @article{manoonpongwoergoetter2009,
      author = {Manoonpong, P. and Wörgötter, F.},
      title = {Efference copies in neural control of dynamic biped walking},
      pages = {1140-1153},
      journal = {Robotics and Autonomous Systems},
      year = {2009},
      volume= {57},
      number = {11},
      abstract = {In the early 1950s, von Holst and Mittelstaedt proposed that motor commands copied within the central nervous system efference copy help to distinguish reafference activity afference activity due to self- generated motion from exafference activity afference activity due to external stimulus. In addition, an efference copy can be also used to compare it with the actual sensory feedback in order to suppress self- generated sensations. Based on these biological findings, we conduct here two experimental studies on our biped RunBot where such principles together with neural forward models are applied to RunBots dynamic locomotion control. The main purpose of this article is to present the modular design of RunBots control architecture and discuss how the inherent dynamic properties of the different modules lead to the required signal processing. We believe that the experimental studies pursued here will sharpen our understanding of how the efference copies influence dynamic locomotion control to the benefit of modern neural control strategies in robots}}
    Abstract: In the early 1950s, von Holst and Mittelstaedt proposed that motor commands copied within the central nervous system efference copy help to distinguish reafference activity afference activity due to self- generated motion from exafference activity afference activity due to external stimulus. In addition, an efference copy can be also used to compare it with the actual sensory feedback in order to suppress self- generated sensations. Based on these biological findings, we conduct here two experimental studies on our biped RunBot where such principles together with neural forward models are applied to RunBots dynamic locomotion control. The main purpose of this article is to present the modular design of RunBots control architecture and discuss how the inherent dynamic properties of the different modules lead to the required signal processing. We believe that the experimental studies pursued here will sharpen our understanding of how the efference copies influence dynamic locomotion control to the benefit of modern neural control strategies in robots
    Review:
    Manoonpong, P. and Wörgötter, F. (2009).
    Adaptive Sensor-Driven Neural Control for Learning in Walking Machines. Neural Information Processing, 47-55, 5864. DOI: 10.1007/978-3-642-10684-2_6.
    BibTeX:
    @inproceedings{manoonpongwoergoetter2009a,
      author = {Manoonpong, P. and Wörgötter, F.},
      title = {Adaptive Sensor-Driven Neural Control for Learning in Walking Machines},
      pages = {47-55},
      booktitle = {Neural Information Processing},
      year = {2009},
      volume= {5864},
      editor = {Leung, ChiSing and Lee, Minho and Chan, Jonathan},
      publisher = {Springer Berlin Heidelberg},
      series = {Lecture Notes in Computer Science},
      doi = {10.1007/978-3-642-10684-2_6},
      abstract = {Wild rodents learn the danger-predicting meaning of preda- tor bird calls through the paring of cues which are an aversive stimulus immediate danger signal or unconditioned stimulus, US and the acous- tic stimulus predator signal or conditioned stimulus, CS. This learning is a form of pavlovian conditioning. In analogy, in this article a setup is described where adaptive sensor-driven neural control is used to simulate biologically-inspired acoustic predator-recognition learning for a safe es- cape on a six-legged walking machine. As a result, the controller allows the walking machine to learn the association of a predictive acoustic sig- nal predator signal, CS and a reflex infrared signal immediate danger signal, US. Such that after learning the machine performs fast walking behavior when hearing an approaching predator from behind leading to safely escape from the attack}}
    Abstract: Wild rodents learn the danger-predicting meaning of preda- tor bird calls through the paring of cues which are an aversive stimulus immediate danger signal or unconditioned stimulus, US and the acous- tic stimulus predator signal or conditioned stimulus, CS. This learning is a form of pavlovian conditioning. In analogy, in this article a setup is described where adaptive sensor-driven neural control is used to simulate biologically-inspired acoustic predator-recognition learning for a safe es- cape on a six-legged walking machine. As a result, the controller allows the walking machine to learn the association of a predictive acoustic sig- nal predator signal, CS and a reflex infrared signal immediate danger signal, US. Such that after learning the machine performs fast walking behavior when hearing an approaching predator from behind leading to safely escape from the attack
    Review:
    Renjewski, D. and Manoonpong, P. and Seyfarth, A. and Wörgötter, F. (2008).
    From Biomechanical Concepts Towards Fast And Robust Robots. Advances in Mobile Robotics: Proc. of 11th CLAWAR, Marques L, Almeida A, Tokhi MO, Virk GS Eds., World Scientific, 630-637.
    BibTeX:
    @inproceedings{renjewskimanoonpongseyfarth2008,
      author = {Renjewski, D. and Manoonpong, P. and Seyfarth, A. and Wörgötter, F.},
      title = {From Biomechanical Concepts Towards Fast And Robust Robots},
      pages = {630-637},
      booktitle = {Advances in Mobile Robotics: Proc. of 11th CLAWAR, Marques L, Almeida A, Tokhi MO, Virk GS Eds., World Scientific},
      year = {2008},
      abstract = {Robots of any kind, highly integrated mechatronic systems, are smart combina- tions of mechanics, electronics and information technology. The development of bipedal robots in particular, which perform human-like locomotion, challenges scientists on even higher levels. Facing this challenge, this article presents a biomimetic bottom-up approach to use knowledge of biomechanical experiments on human walking and running, computer simulation and neuronal control concepts to sequentially design highly adaptable and compliant walking machines}}
    Abstract: Robots of any kind, highly integrated mechatronic systems, are smart combina- tions of mechanics, electronics and information technology. The development of bipedal robots in particular, which perform human-like locomotion, challenges scientists on even higher levels. Facing this challenge, this article presents a biomimetic bottom-up approach to use knowledge of biomechanical experiments on human walking and running, computer simulation and neuronal control concepts to sequentially design highly adaptable and compliant walking machines
    Review:
    Manoonpong, P. and Wörgötter, F. (2008).
    Using efference copy for external and self-generated sensory noise cancellation. Proceedings of 4th International Symposium on Adaptive Motion of Animals and Machines, Case Western Reserve University, Cleveland OH-USA, 227-228.
    BibTeX:
    @inproceedings{manoonpongwoergoetter2008,
      author = {Manoonpong, P. and Wörgötter, F.},
      title = {Using efference copy for external and self-generated sensory noise cancellation},
      pages = {227-228},
      booktitle = {Proceedings of 4th International Symposium on Adaptive Motion of Animals and Machines, Case Western Reserve University, Cleveland OH-USA},
      year = {2008},
      url = {http://amam.case.edu/AMAM2008}}
    Abstract:
    Review:
    Manoonpong, P. and Wörgötter, F. (2008).
    Biologically-Inspired Reactive Walking Machine AMOS-WD06. In Proceedings of 4th International Symposium on Adaptive Motion of Animals and Machines AMAM2008, 240-241.
    BibTeX:
    @inproceedings{manoonpongwoergoetter2008a,
      author = {Manoonpong, P. and Wörgötter, F.},
      title = {Biologically-Inspired Reactive Walking Machine AMOS-WD06},
      pages = {240-241},
      booktitle = {In Proceedings of 4th International Symposium on Adaptive Motion of Animals and Machines AMAM2008},
      year = {2008},
      abstract = {The six-legged walking machine AMOS-WD061 see Fig. 1A is a hardware platform for studying the coordination of many degrees of freedom, for performing experiments with neural controllers, and for the development of artificial perception-action systems}}
    Abstract: The six-legged walking machine AMOS-WD061 see Fig. 1A is a hardware platform for studying the coordination of many degrees of freedom, for performing experiments with neural controllers, and for the development of artificial perception-action systems
    Review:
    Manoonpong, P. and Wörgötter, F. (2008).
    Neural Control for Locomotion of Walking Machines. In Proceedings of 4th International Symposium on Adaptive Motion of Animals and Machines AMAM2008, 54-55.
    BibTeX:
    @inproceedings{manoonpongwoergoetter2008b,
      author = {Manoonpong, P. and Wörgötter, F.},
      title = {Neural Control for Locomotion of Walking Machines},
      pages = {54-55},
      booktitle = {In Proceedings of 4th International Symposium on Adaptive Motion of Animals and Machines AMAM2008},
      year = {2008},
      abstract = {The basic locomotion and rhythm of stepping in walking animals like cockroaches mostly relies on a central pattern generator CPG 1, while their peripheral sensors are used to control walking behaviors 2. By contrast, in stick insects, sensory feedback serving as reflexive mechanism plays a critical role in shaping the motor pattern for adaptivity and robustness of walking gaits 2. Inspired by the principles of biological locomotion control, two different types of neural mechanism for locomotion control of walking machines are presented. One is called modular reactive neural control and the other is adaptive reflex neural control}}
    Abstract: The basic locomotion and rhythm of stepping in walking animals like cockroaches mostly relies on a central pattern generator CPG 1, while their peripheral sensors are used to control walking behaviors 2. By contrast, in stick insects, sensory feedback serving as reflexive mechanism plays a critical role in shaping the motor pattern for adaptivity and robustness of walking gaits 2. Inspired by the principles of biological locomotion control, two different types of neural mechanism for locomotion control of walking machines are presented. One is called modular reactive neural control and the other is adaptive reflex neural control
    Review:
    Manoonpong, P. and Pasemann, F. and Wörgötter, F. (2008).
    Sensor-driven neural control for omnidirectional locomotion and versatile reactive behaviors of walking machines. Robotics and Autonomous Systems, 265-288, 56, 3.
    BibTeX:
    @article{manoonpongpasemannwoergoetter2008,
      author = {Manoonpong, P. and Pasemann, F. and Wörgötter, F.},
      title = {Sensor-driven neural control for omnidirectional locomotion and versatile reactive behaviors of walking machines},
      pages = {265-288},
      journal = {Robotics and Autonomous Systems},
      year = {2008},
      volume= {56},
      number = {3},
      abstract = {This article describes modular neural control structures for different walking machines utilizing discrete-time neurodynamics. A simple neural oscillator network serves as a central pattern generator producing the basic rhythmic leg movements. Other modules, like the velocity regulating and the phase switching networks, enable the machines to perform omnidirectional walking as well as reactive behaviors, like obstacle avoidance and different types of tropisms. These behaviors are generated in a sensori-motor loop with respect to appropriate sensor inputs, to which a neural preprocessing is applied. The neuromodules presented are small so that their structure function relationship can be analysed. The complete controller is general in the sense that it can be easily adapted to different types of even-legged walking machines without changing its internal structure and parameters}}
    Abstract: This article describes modular neural control structures for different walking machines utilizing discrete-time neurodynamics. A simple neural oscillator network serves as a central pattern generator producing the basic rhythmic leg movements. Other modules, like the velocity regulating and the phase switching networks, enable the machines to perform omnidirectional walking as well as reactive behaviors, like obstacle avoidance and different types of tropisms. These behaviors are generated in a sensori-motor loop with respect to appropriate sensor inputs, to which a neural preprocessing is applied. The neuromodules presented are small so that their structure function relationship can be analysed. The complete controller is general in the sense that it can be easily adapted to different types of even-legged walking machines without changing its internal structure and parameters
    Review:
    Manoonpong, P. and Pasemann, F. and Wörgötter, F. (2007).
    Reactive Neural Control for Phototaxis and Obstacle Avoidance Behavior of Walking Machines. Proceedings of World Academy of Science, Engineering and Technology PWASET, International conference on Intelligent systems ICIS 07, Bangkok, Thailand, December, 14-16.
    BibTeX:
    @inproceedings{manoonpongpasemannwoergoetter2007,
      author = {Manoonpong, P. and Pasemann, F. and Wörgötter, F.},
      title = {Reactive Neural Control for Phototaxis and Obstacle Avoidance Behavior of Walking Machines},
      pages = {14-16},
      booktitle = {Proceedings of World Academy of Science, Engineering and Technology PWASET, International conference on Intelligent systems ICIS 07, Bangkok, Thailand, December},
      year = {2007}}
    Abstract:
    Review:
    Manoonpong, P. and Geng, T. and Porr, B. and Wörgötter, F. (2007).
    The RunBot architecture for adaptive, fast, dynamic walking. IEEE International Symposium on Circuits and Systems ISCAS, New Orleans, USA, 1181-1184.
    BibTeX:
    @inproceedings{manoonponggengporr2007,
      author = {Manoonpong, P. and Geng, T. and Porr, B. and Wörgötter, F.},
      title = {The RunBot architecture for adaptive, fast, dynamic walking},
      pages = {1181-1184},
      booktitle = {IEEE International Symposium on Circuits and Systems ISCAS, New Orleans, USA},
      year = {2007}}
    Abstract:
    Review:
    Manoonpong, P. and Geng, T. and Porr, B. and Kulvicius, T. and Wörgötter, F. (2007).
    Adaptive, Fast Walking in a Biped Robot under Neuronal Control and Learning. Public Library of Science Computational Biology PLoS Comp. Biol., 37, e134. DOI: 10.1371/journal.pcbi.0030134.
    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},
      journal = {Public Library of Science Computational Biology PLoS Comp. Biol., 37, e134},
      year = {2007},
      doi = {10.1371/journal.pcbi.0030134},
      abstract = {Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori-motor loops where the walking process provides feedback signals to the walkers sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks.}}
    Abstract: Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori-motor loops where the walking process provides feedback signals to the walkers sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks.
    Review:
    Xiong, X. and Wörgötter, F. and Manoonpong, P. (2014).
    Virtual Agonist-antagonist Mechanisms Produce Biological Muscle-like Functions: An Application for Robot Joint Control. Industrial Robot: An International Journal, 340 - 346, 41, 4. DOI: 10.1108/IR-11-2013-421.
    BibTeX:
    @article{xiongwoergoettermanoonpong2014,
      author = {Xiong, X. and Wörgötter, F. and Manoonpong, P.},
      title = {Virtual Agonist-antagonist Mechanisms Produce Biological Muscle-like Functions: An Application for Robot Joint Control},
      pages = {340 - 346},
      journal = {Industrial Robot: An International Journal},
      year = {2014},
      volume= {41},
      number = {4},
      publisher = {Emerald Group Publishing Ltd.},
      url = {http://www.emeraldinsight.com/doi/abs/10.1108/IR-11-2013-421},
      doi = {10.1108/IR-11-2013-421},
      abstract = {Purpose - Biological muscles of animals have a surprising variety of functions, i.e., struts, springs, and brakes. According to this, the purpose of this paper is to apply virtual agonist-antagonist mechanisms to robot joint control allowing for muscle-like functions and variably compliant joint motions. Design/methodology/approach - Each joint is driven by a pair of virtual agonist-antagonist mechanism (VAAM, i.e., passive components). The muscle-like functions as well as the variable joint compliance are simply achieved by tuning the damping coefficient of the VAAM. Findings - With the VAAM, variably compliant joint motions can be produced without mechanically bulky and complex mechanisms or complex force/toque sensing at each joint. Moreover, through tuning the damping coefficient of the VAAM, the functions of the VAAM are comparable to biological muscles. Originality/value - The model (i.e., VAAM) provides a way forward to emulate muscle-like functions that are comparable to those found in physiological experiments of biological muscles. Based on these muscle-like functions, the robotic joints can easily achieve variable compliance that does not require complex physical components or torque sensing systems thereby capable of implementing the model on small legged robots driven by, e.g., standard servo motors. Thus, the VAAM minimizes hardware and reduces system complexity. From this point of view, the model opens up another way of simulating muscle behaviors on artificial machines. Executive summary The VAAM can be applied to produce variable compliant motions of a high DOF robot. Only relying on force sensing at the end effector, this application is easily achieved by changing coefficients of the VAAM. Therefore, the VAAM can reduce economic cost on mechanical and sensing components of the robot, compared to traditional methods (e.g., artificial muscles).}}
    Abstract: Purpose - Biological muscles of animals have a surprising variety of functions, i.e., struts, springs, and brakes. According to this, the purpose of this paper is to apply virtual agonist-antagonist mechanisms to robot joint control allowing for muscle-like functions and variably compliant joint motions. Design/methodology/approach - Each joint is driven by a pair of virtual agonist-antagonist mechanism (VAAM, i.e., passive components). The muscle-like functions as well as the variable joint compliance are simply achieved by tuning the damping coefficient of the VAAM. Findings - With the VAAM, variably compliant joint motions can be produced without mechanically bulky and complex mechanisms or complex force/toque sensing at each joint. Moreover, through tuning the damping coefficient of the VAAM, the functions of the VAAM are comparable to biological muscles. Originality/value - The model (i.e., VAAM) provides a way forward to emulate muscle-like functions that are comparable to those found in physiological experiments of biological muscles. Based on these muscle-like functions, the robotic joints can easily achieve variable compliance that does not require complex physical components or torque sensing systems thereby capable of implementing the model on small legged robots driven by, e.g., standard servo motors. Thus, the VAAM minimizes hardware and reduces system complexity. From this point of view, the model opens up another way of simulating muscle behaviors on artificial machines. Executive summary The VAAM can be applied to produce variable compliant motions of a high DOF robot. Only relying on force sensing at the end effector, this application is easily achieved by changing coefficients of the VAAM. Therefore, the VAAM can reduce economic cost on mechanical and sensing components of the robot, compared to traditional methods (e.g., artificial muscles).
    Review:
    Manoonpong, P. and Geng, T. and Wörgötter, F. (2006).
    Exploring the dynamic walking range of the biped robot Runbot with an active upper-body component. IEEE-RAS International Conference on Humanoid Robots Humanoids 2006, 418-424.
    BibTeX:
    @inproceedings{manoonponggengwoergoetter2006,
      author = {Manoonpong, P. and Geng, T. and Wörgötter, F.},
      title = {Exploring the dynamic walking range of the biped robot Runbot with an active upper-body component},
      pages = {418-424},
      booktitle = {IEEE-RAS International Conference on Humanoid Robots Humanoids 2006},
      year = {2006}}
    Abstract:
    Review:
    Goldschmidt, D. and Wörgötter, F. and Manoonpong, P. (2014).
    Biologically-Inspired Adaptive Obstacle Negotiation Behavior of Hexapod Robots. Frontiers in Neurorobotics, 1 -- 16, 8, 3. DOI: 10.3389/fnbot.2014.00003.
    BibTeX:
    @article{goldschmidtwoergoettermanoonpong201,
      author = {Goldschmidt, D. and Wörgötter, F. and Manoonpong, P.},
      title = {Biologically-Inspired Adaptive Obstacle Negotiation Behavior of Hexapod Robots},
      pages = {1 -- 16},
      journal = {Frontiers in Neurorobotics},
      year = {2014},
      volume= {8},
      number = {3},
      url = {http://journal.frontiersin.org/Journal/10.3389/fnbot.2014.00003/abstract},
      doi = {10.3389/fnbot.2014.00003},
      abstract = {Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal conditioned stimulus, CS and a late, reflex signal unconditioned stimulus, UCS, both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully transferred to a real hexapod robot, called AMOS II. The results show that the robot can efficiently negotiate obstacles with a height up to 85% of the robots leg length in simulation and 75% in a real environment}}
    Abstract: Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal conditioned stimulus, CS and a late, reflex signal unconditioned stimulus, UCS, both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully transferred to a real hexapod robot, called AMOS II. The results show that the robot can efficiently negotiate obstacles with a height up to 85% of the robots leg length in simulation and 75% in a real environment
    Review:
    Manoonpong, P. and Dasgupta, S. and Goldschmidt, D. and Wörgötter, F. (2014).
    Reservoir-based online adaptive forward models with neural control for complex locomotion in a hexapod robot. International Joint Conference on Neural Networks (IJCNN), 3295-3302. DOI: 10.1109/IJCNN.2014.6889405.
    BibTeX:
    @inproceedings{manoonpongdasguptagoldschmidt2014,
      author = {Manoonpong, P. and Dasgupta, S. and Goldschmidt, D. and Wörgötter, F.},
      title = {Reservoir-based online adaptive forward models with neural control for complex locomotion in a hexapod robot},
      pages = {3295-3302},
      booktitle = {International Joint Conference on Neural Networks (IJCNN)},
      year = {2014},
      month = {July},
      doi = {10.1109/IJCNN.2014.6889405},
      abstract = {Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control for generating basic rhythmic patterns and coordinated movements, 2) reservoir-based adaptive forward models with efference copies for sensory prediction as well as state estimation, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Simulation results show that this bio-inspired approach allows the walking robot to perform complex locomotor abilities including walking on undulated terrains, crossing a large gap, as well as climbing over a high obstacle and a fleet of stairs.}}
    Abstract: Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control for generating basic rhythmic patterns and coordinated movements, 2) reservoir-based adaptive forward models with efference copies for sensory prediction as well as state estimation, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Simulation results show that this bio-inspired approach allows the walking robot to perform complex locomotor abilities including walking on undulated terrains, crossing a large gap, as well as climbing over a high obstacle and a fleet of stairs.
    Review:
    Zeidan, B. and Dasgupta, S. and Wörgötter, F. and Manoonpong, P. (2014).
    Adaptive Landmark-Based Navigation System Using Learning Techniques. From Animals to Animats 13, 121-131, 8575. DOI: 10.1007/978-3-319-08864-8_12.
    BibTeX:
    @inproceedings{zeidandasguptawoergoetter2014,
      author = {Zeidan, B. and Dasgupta, S. and Wörgötter, F. and Manoonpong, P.},
      title = {Adaptive Landmark-Based Navigation System Using Learning Techniques},
      pages = {121-131},
      booktitle = {From Animals to Animats 13},
      year = {2014},
      volume= {8575},
      editor = {del Pobil, AngelP. and Chinellato, Eris and Martinez-Martin, Ester and Hallam, John and Cervera, Enric and Morales, Antonio},
      language = {English},
      month = {July},
      publisher = {Springer International Publishing},
      series = {Lecture Notes in Computer Science},
      url = {http://dx.doi.org/10.1007/978-3-319-08864-8_12},
      doi = {10.1007/978-3-319-08864-8_12},
      abstract = {The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal. Inspired by this, we develop an adaptive landmark-based navigation system based on sequential reinforcement learning. In addition, correlation-based learning is also integrated into the system to improve learning performance. The proposed system has been applied to simulated simple wheeled and more complex hexapod robots. As a result, it allows the robots to successfully learn to navigate to distal goals in complex environments.}}
    Abstract: The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal. Inspired by this, we develop an adaptive landmark-based navigation system based on sequential reinforcement learning. In addition, correlation-based learning is also integrated into the system to improve learning performance. The proposed system has been applied to simulated simple wheeled and more complex hexapod robots. As a result, it allows the robots to successfully learn to navigate to distal goals in complex environments.
    Review:
    Ren, G. and Chen, W. and Dasgupta, S. and Kolodziejski, C. and Wörgötter, F. and Manoonpong, P. (2014).
    Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation. Information Sciences, 666 - 682, 294. DOI: 10.1016/j.ins.2014.05.001.
    BibTeX:
    @article{renchendasgupta2014,
      author = {Ren, G. and Chen, W. and Dasgupta, S. and Kolodziejski, C. and Wörgötter, F. and Manoonpong, P.},
      title = {Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation},
      pages = {666 - 682},
      journal = {Information Sciences},
      year = {2014},
      volume= {294},
      month = {05},
      publisher = {Elseiver},
      url = {http://www.sciencedirect.com/science/article/pii/S0020025514005192},
      doi = {10.1016/j.ins.2014.05.001},
      abstract = {An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robots locomotion control as a central pattern generator CPG, sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation}}
    Abstract: An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robots locomotion control as a central pattern generator CPG, sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation
    Review:
    Dasgupta, S. and Wörgötter, F. and Manoonpong, P. (2013).
    Active Memory in Input Driven Recurrent Neural Networks. Bernstein Conference 2013. DOI: 10.12751/nncn.bc2013.0151.
    BibTeX:
    @inproceedings{dasguptawoergoettermanoonpong2013a,
      author = {Dasgupta, S. and Wörgötter, F. and Manoonpong, P.},
      title = {Active Memory in Input Driven Recurrent Neural Networks},
      booktitle = {Bernstein Conference 2013},
      year = {2013},
      doi = {10.12751/nncn.bc2013.0151},
      abstract = {Understanding the exact mechanism of learning and memory emerging from complex dynamical systems like neural networks serves as a challenging field of research. Traditionally the neural mechanisms underlying memory and cognition in these systems are described by steady-state or stable fixed point attractor dynamics. However an alternative and refined understanding of the neuronal dynamics can be achieved through the idea of transient dynamics 1 (reservoir computing paradigm) i.e., computation through input specific trajectories in neural space without stable equilibrium. Mathematical analysis of the underlying memory through such transient dynamics is difficult. As such information theory provides tools to quantify the dynamics of memory in such networks. One such popular measure of memory capacity in reservoir networks is the linear memory capacity 2. It provides an indication of how well the network can reconstruct delayed versions of the input signal. However it assumes a linear retrieval of input signal and deteriorates with neuron non-linearity. Alternatively, active information storage 3 provides a measure of local neuron memory by quantifying the degree of influence of past activity on the next time step activity of a neuron independent of neuronal non-linearity. In this work we further extend this quantity by calculating the mutual information between a neuron past activity and its immediate future activity while conditioning out delayed versions of the input signal. Summing over different delays of input signal it provides a suitable measure of total input driven active memory in the network. Intuitively active memory calculates the actual memory in use i.e. influence of input history on local neuron memory. We compare memory capacity and active memory (AM) with different network parameters for networks driven with statistically different inputs and justify AM as an appropriate means to quantify the dynamics of memory in input driven neural networks.}}
    Abstract: Understanding the exact mechanism of learning and memory emerging from complex dynamical systems like neural networks serves as a challenging field of research. Traditionally the neural mechanisms underlying memory and cognition in these systems are described by steady-state or stable fixed point attractor dynamics. However an alternative and refined understanding of the neuronal dynamics can be achieved through the idea of transient dynamics 1 (reservoir computing paradigm) i.e., computation through input specific trajectories in neural space without stable equilibrium. Mathematical analysis of the underlying memory through such transient dynamics is difficult. As such information theory provides tools to quantify the dynamics of memory in such networks. One such popular measure of memory capacity in reservoir networks is the linear memory capacity 2. It provides an indication of how well the network can reconstruct delayed versions of the input signal. However it assumes a linear retrieval of input signal and deteriorates with neuron non-linearity. Alternatively, active information storage 3 provides a measure of local neuron memory by quantifying the degree of influence of past activity on the next time step activity of a neuron independent of neuronal non-linearity. In this work we further extend this quantity by calculating the mutual information between a neuron past activity and its immediate future activity while conditioning out delayed versions of the input signal. Summing over different delays of input signal it provides a suitable measure of total input driven active memory in the network. Intuitively active memory calculates the actual memory in use i.e. influence of input history on local neuron memory. We compare memory capacity and active memory (AM) with different network parameters for networks driven with statistically different inputs and justify AM as an appropriate means to quantify the dynamics of memory in input driven neural networks.
    Review:
    Kuhlemann, I. and Braun, J -M. and Wörgötter, F. and Manoonpong, P. (2014).
    Comparing Arc-shaped Feet and Rigid Ankles with Flat Feet and Compliant Ankles for a Dynamic Walker. Mobile Service RoboticsWSPC Proceedings, 353-360, 17. DOI: 10.1142/9789814623353_0041.
    BibTeX:
    @inproceedings{kuhlemannbraunwoergoetter2014,
      author = {Kuhlemann, I. and Braun, J -M. and Wörgötter, F. and Manoonpong, P.},
      title = {Comparing Arc-shaped Feet and Rigid Ankles with Flat Feet and Compliant Ankles for a Dynamic Walker},
      pages = {353-360},
      booktitle = {Mobile Service Robotics},
      journal = {WSPC Proceedings},
      year = {2014},
      number = {17},
      language = {English},
      location = {Poznan, Poland},
      series = {Proceedings of the International Conference on Climbing and Walking Robots},
      url = {http://www.bfnt-goettingen.de/Publications/articlereference.2014-10-23.5545515863},
      doi = {10.1142/9789814623353_0041},
      abstract = {In this paper we show that exchanging curved feet and rigid ankles by at feet and compliant ankles improves the range of gait parameters for a bipedal dynamic walker. The new lower legs were designed such that they t to the old set-up, allowing for a direct and quantitative comparison. The dynamic walking robot RunBot, controlled by an re exive neural network, uses only few sensors for generating its stable gait. The results show that at feet and compliant ankles extend RunBots parameter range especially to more leaning back postures. They also allow the robot to stably walk over obstacles with low height.}}
    Abstract: In this paper we show that exchanging curved feet and rigid ankles by at feet and compliant ankles improves the range of gait parameters for a bipedal dynamic walker. The new lower legs were designed such that they t to the old set-up, allowing for a direct and quantitative comparison. The dynamic walking robot RunBot, controlled by an re exive neural network, uses only few sensors for generating its stable gait. The results show that at feet and compliant ankles extend RunBots parameter range especially to more leaning back postures. They also allow the robot to stably walk over obstacles with low height.
    Review:
    Braun, J -M. and Wörgötter, F. and Manoonpong, P. (2014).
    Internal Models Support Specific Gaits in Orthotic Devices. Mobile Service Robotics, 539-546, 17. DOI: 10.1142/9789814623353_0063.
    BibTeX:
    @inproceedings{braunwoergoettermanoonpong2014a,
      author = {Braun, J -M. and Wörgötter, F. and Manoonpong, P.},
      title = {Internal Models Support Specific Gaits in Orthotic Devices},
      pages = {539-546},
      booktitle = {Mobile Service Robotics},
      year = {2014},
      number = {17},
      language = {English},
      location = {Poznan, Poland},
      series = {Proceedings of the International Conference on Climbing and Walking Robots},
      url = {http://www.worldscientific.com/doi/abs/10.1142/9789814623353_0063},
      doi = {10.1142/9789814623353_0063},
      abstract = {Patients use orthoses and prosthesis for the lower limbs to support and enable movements, they can not or only with difficulties perform themselves. Because traditional devices support only a limited set of movements, patients are restricted in their mobility. A possible approach to overcome such limitations is to supply the patient via the orthosis with situation-dependent gait models. To achieve this, we present a method for gait recognition using model invalidation. We show that these models are capable to predict the individual patients movements and supply the correct gait. We investigate the systems accuracy and robustness on a Knee-Ankle-Foot-Orthosis, introducing behaviour changes depending on the patients current walking situation. We conclude that the here presented model-based support of different gaits has the power to enhance the patients mobility.}}
    Abstract: Patients use orthoses and prosthesis for the lower limbs to support and enable movements, they can not or only with difficulties perform themselves. Because traditional devices support only a limited set of movements, patients are restricted in their mobility. A possible approach to overcome such limitations is to supply the patient via the orthosis with situation-dependent gait models. To achieve this, we present a method for gait recognition using model invalidation. We show that these models are capable to predict the individual patients movements and supply the correct gait. We investigate the systems accuracy and robustness on a Knee-Ankle-Foot-Orthosis, introducing behaviour changes depending on the patients current walking situation. We conclude that the here presented model-based support of different gaits has the power to enhance the patients mobility.
    Review:
    Braun, J. and Wörgötter, F. and Manoonpong, P. (2014).
    Orthosis Controller with Internal Models Supports Individual Gaits. Proceedings of the 9th Annual Dynamic Walking Conference, 1 --2, 9.
    BibTeX:
    @inproceedings{braunwoergoettermanoonpong2014,
      author = {Braun, J. and Wörgötter, F. and Manoonpong, P.},
      title = {Orthosis Controller with Internal Models Supports Individual Gaits},
      pages = {1 --2},
      booktitle = {Proceedings of the 9th Annual Dynamic Walking Conference},
      year = {2014},
      number = {9},
      language = {English},
      location = {Zürich, Switzerland},
      month = {06},
      series = {Proceedings of the 9th Annual Dynamic Walking Conference}}
    Abstract:
    Review:
    Dasgupta, S. and Wörgötter, F. and Manoonpong, P. (2014).
    Neuromodulatory Adaptive Combination of Correlation-based Learning in Cerebellum and Reward-based Learning in Basal Ganglia for Goal-directed Behavior Control. Frontiers in Neural Circuits, 1 -- 21, 8, 00126. DOI: 10.3389/fncir.2014.00126.
    BibTeX:
    @bibtexentrytype{dasguptawoergoettermanoonpong2014,
      author = {Dasgupta, S. and Wörgötter, F. and Manoonpong, P.},
      title = {Neuromodulatory Adaptive Combination of Correlation-based Learning in Cerebellum and Reward-based Learning in Basal Ganglia for Goal-directed Behavior Control},
      pages = {1 -- 21},
      journal = {Frontiers in Neural Circuits},
      year = {2014},
      volume= {8},
      number = {00126},
      url = {http://journal.frontiersin.org/Journal/10.3389/fncir.2014.00126/abstract},
      doi = {10.3389/fncir.2014.00126},
      abstract = {Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point towards their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms.}}
    Abstract: Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point towards their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms.
    Review:
    Xiong, X. and Wörgötter, F. and Manoonpong, P. (2014).
    Neuromechanical control for hexapedal robot walking on challenging surfaces and surface classification. Robotics and Autonomous Systems, 1777 - 1789, 62, 12. DOI: 10.1016/j.robot.2014.07.008.
    BibTeX:
    @article{xiongwoergoettermanoonpong2014a,
      author = {Xiong, X. and Wörgötter, F. and Manoonpong, P.},
      title = {Neuromechanical control for hexapedal robot walking on challenging surfaces and surface classification},
      pages = {1777 - 1789},
      journal = {Robotics and Autonomous Systems},
      year = {2014},
      volume= {62},
      number = {12},
      url = {http://www.sciencedirect.com/science/article/pii/S0921889014001353},
      doi = {10.1016/j.robot.2014.07.008},
      abstract = {The neuromechanical control principles of animal locomotion provide good insights for the development of bio-inspired legged robots for walking on challenging surfaces. Based on such principles, we developed a neuromechanical controller consisting of a modular neural network (MNN) and of virtual agonist-antagonist muscle mechanisms (VAAMs). The controller allows for variable compliant leg motions of a hexapod robot, thereby leading to energy-efficient walking on different surfaces. Without any passive mechanisms or torque and position feedback at each joint, the variable compliant leg motions are achieved by only changing the stiffness parameters of the VAAMs. In addition, six surfaces can be also classified by observing the motor signals generated by the controller. The performance of the controller is tested on a physical hexapod robot. Experimental results show that it can effectively walk on six different surfaces with the specific resistances between 9.1 and 25.0, and also classify them with high accuracy.}}
    Abstract: The neuromechanical control principles of animal locomotion provide good insights for the development of bio-inspired legged robots for walking on challenging surfaces. Based on such principles, we developed a neuromechanical controller consisting of a modular neural network (MNN) and of virtual agonist-antagonist muscle mechanisms (VAAMs). The controller allows for variable compliant leg motions of a hexapod robot, thereby leading to energy-efficient walking on different surfaces. Without any passive mechanisms or torque and position feedback at each joint, the variable compliant leg motions are achieved by only changing the stiffness parameters of the VAAMs. In addition, six surfaces can be also classified by observing the motor signals generated by the controller. The performance of the controller is tested on a physical hexapod robot. Experimental results show that it can effectively walk on six different surfaces with the specific resistances between 9.1 and 25.0, and also classify them with high accuracy.
    Review:
    Manoonpong, P. and Goldschmidt, D. and Wörgötter, F. and Kovalev, A. and Heepe, L. and Gorb, S. (2013).
    Using a Biological Material to Improve Locomotion of Hexapod Robots. Biomimetic and Biohybrid Systems, 402-404, 8064. DOI: 10.1007/978-3-642-39802-5_48.
    BibTeX:
    @incollection{manoonponggoldschmidtwoergoetter201,
      author = {Manoonpong, P. and Goldschmidt, D. and Wörgötter, F. and Kovalev, A. and Heepe, L. and Gorb, S.},
      title = {Using a Biological Material to Improve Locomotion of Hexapod Robots},
      pages = {402-404},
      booktitle = {Biomimetic and Biohybrid Systems},
      year = {2013},
      volume= {8064},
      editor = {Lepora, NathanF. and Mura, Anna and Krapp, HolgerG. and Verschure, PaulF.M.J. and Prescott, TonyJ.},
      language = {English},
      publisher = {Springer Berlin Heidelberg},
      series = {Lecture Notes in Computer Science},
      url = {http://dx.doi.org/10.1007/978-3-642-39802-5_48},
      doi = {10.1007/978-3-642-39802-5_48},
      abstract = {Animals can move in not only elegant but also energy efficient ways. Their skin is one of the key components for this achievement. It provides a proper friction for forward motion and can protect them from slipping on a surface during locomotion. Inspired by this, we applied real shark skin to the foot soles of our hexapod robot AMOS. The material is formed to cover each foot of AMOS. Due to shark skin texture which has asymmetric profile inducing frictional anisotropy, this feature allows AMOS to grip specific surfaces and effectively locomote without slipping. Using real-time walking experiments, this study shows that implementing the biological material on the robot can reduce energy consumption while walking up a steep slope covered by carpets or other felt-like or rough substrates.}}
    Abstract: Animals can move in not only elegant but also energy efficient ways. Their skin is one of the key components for this achievement. It provides a proper friction for forward motion and can protect them from slipping on a surface during locomotion. Inspired by this, we applied real shark skin to the foot soles of our hexapod robot AMOS. The material is formed to cover each foot of AMOS. Due to shark skin texture which has asymmetric profile inducing frictional anisotropy, this feature allows AMOS to grip specific surfaces and effectively locomote without slipping. Using real-time walking experiments, this study shows that implementing the biological material on the robot can reduce energy consumption while walking up a steep slope covered by carpets or other felt-like or rough substrates.
    Review:
    Dasgupta, S. and Wörgötter, F. and Manoonpong, P. (2014).
    Goal-directed Learning with Reward Modulated Interaction between Striatal and Cerebellar Systems. Bernstein Conference 2014, 1 -- 1. DOI: 10.12751/nncn.bc2014.0177.
    BibTeX:
    @inproceedings{dasguptawoergoettermanoonpong2014a,
      author = {Dasgupta, S. and Wörgötter, F. and Manoonpong, P.},
      title = {Goal-directed Learning with Reward Modulated Interaction between Striatal and Cerebellar Systems},
      pages = {1 -- 1},
      booktitle = {Bernstein Conference 2014},
      year = {2014},
      month = {Sept},
      doi = {10.12751/nncn.bc2014.0177},
      abstract = {Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation based learning) and operand conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia (striatal system) towards reward-based learning, where as the cerebellum evidently plays an important role in developing specific conditioned responses. Although, they are viewed as distinct learning systems 1, recent animal experiments point towards their complementary role in behavioral learning, and also show the existence of substantial two-way communication between the two structures 2. Based on this notion of co-operative learning, in this work we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and compete with each other (Figure 1). We envision such an interaction being driven by a simple reward modulated heterosynaptic plasticity (RMHP) rule 3, in order to guide the over all goal-directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and feed-forward correlation learning model of the cerebellum (input correlation learning-ICO) 4, we demonstrate that the RMHP rule can effectively combine the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled animat in a dynamic foraging task. Although, they are modeled within a highly simplified level of biological abstraction, we clearly demonstrate that such a combined learning mechanism, leads to much stabler and faster learning of goal-directed behaviors in comparison to the individual systems.}}
    Abstract: Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation based learning) and operand conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia (striatal system) towards reward-based learning, where as the cerebellum evidently plays an important role in developing specific conditioned responses. Although, they are viewed as distinct learning systems 1, recent animal experiments point towards their complementary role in behavioral learning, and also show the existence of substantial two-way communication between the two structures 2. Based on this notion of co-operative learning, in this work we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and compete with each other (Figure 1). We envision such an interaction being driven by a simple reward modulated heterosynaptic plasticity (RMHP) rule 3, in order to guide the over all goal-directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and feed-forward correlation learning model of the cerebellum (input correlation learning-ICO) 4, we demonstrate that the RMHP rule can effectively combine the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled animat in a dynamic foraging task. Although, they are modeled within a highly simplified level of biological abstraction, we clearly demonstrate that such a combined learning mechanism, leads to much stabler and faster learning of goal-directed behaviors in comparison to the individual systems.
    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:
    Goldschmidt, D. and Dasgupta, S. and Wörgötter, F. and Manoonpong, P. (2015).
    A Neural Path Integration Mechanism for Adaptive Vector Navigation in Autonomous Agents. International Joint Conference on Neural Networks (IJCNN), neural path integration mechanism for adaptive vector navigation in autonomous agents, 1-8. DOI: 10.1109/IJCNN.2015.7280400.
    BibTeX:
    @inproceedings{goldschmidtdasguptawoergoetter2015,
      author = {Goldschmidt, D. and Dasgupta, S. and Wörgötter, F. and Manoonpong, P.},
      title = {A Neural Path Integration Mechanism for Adaptive Vector Navigation in Autonomous Agents},
      pages = {1-8},
      booktitle = {International Joint Conference on Neural Networks (IJCNN), neural path integration mechanism for adaptive vector navigation in autonomous agents},
      year = {2015},
      month = {July},
      doi = {10.1109/IJCNN.2015.7280400},
      abstract = {Animals show remarkable capabilities in navigating their habitat in a fully autonomous and energy-efficient way. In many species, these capabilities rely on a process called path integration, which enables them to estimate their current location and to find their way back home after long-distance journeys. Path integration is achieved by integrating compass and odometric cues. Here we introduce a neural path integration mechanism that interacts with a neural locomotion control to simulate homing behavior and path integration-related behaviors observed in animals. The mechanism is applied to a simulated six-legged artificial agent. Input signals from an allothetic compass and odometry are sustained through leaky neural integrator circuits, which are then used to compute the home vector by local excitation-global inhibition interactions. The home vector is computed and represented in circular arrays of neurons, where compass directions are population-coded and linear displacements are rate-coded. The mechanism allows for robust homing behavior in the presence of external sensory noise. The emergent behavior of the controlled agent does not only show a robust solution for the problem of autonomous agent navigation, but it also reproduces various aspects of animal navigation. Finally, we discuss how the proposed path integration mechanism may be used as a scaffold for spatial learning in terms of vector navigation.}}
    Abstract: Animals show remarkable capabilities in navigating their habitat in a fully autonomous and energy-efficient way. In many species, these capabilities rely on a process called path integration, which enables them to estimate their current location and to find their way back home after long-distance journeys. Path integration is achieved by integrating compass and odometric cues. Here we introduce a neural path integration mechanism that interacts with a neural locomotion control to simulate homing behavior and path integration-related behaviors observed in animals. The mechanism is applied to a simulated six-legged artificial agent. Input signals from an allothetic compass and odometry are sustained through leaky neural integrator circuits, which are then used to compute the home vector by local excitation-global inhibition interactions. The home vector is computed and represented in circular arrays of neurons, where compass directions are population-coded and linear displacements are rate-coded. The mechanism allows for robust homing behavior in the presence of external sensory noise. The emergent behavior of the controlled agent does not only show a robust solution for the problem of autonomous agent navigation, but it also reproduces various aspects of animal navigation. Finally, we discuss how the proposed path integration mechanism may be used as a scaffold for spatial learning in terms of vector navigation.
    Review:
    Dasgupta, S. and Goldschmidt, D. and Wörgötter, F. and Manoonpong, P. (2015).
    Distributed Recurrent Neural Forward Models with Synaptic Adaptation for Complex Behaviors of Walking Robots. arXiv preprint arXiv:1506.03599.
    BibTeX:
    @article{dasguptagoldschmidtwoergoetter2015,
      author = {Dasgupta, S. and Goldschmidt, D. and Wörgötter, F. and Manoonpong, P.},
      title = {Distributed Recurrent Neural Forward Models with Synaptic Adaptation for Complex Behaviors of Walking Robots},
      journal = {arXiv preprint arXiv:1506.03599},
      year = {2015},
      url = {http://arxiv.org/abs/1506.03599},
      abstract = {Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biome- chanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of in- ternal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively com- bines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of 1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, 2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex loco- motive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps as well as climbing over high obstacles.}}
    Abstract: Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biome- chanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of in- ternal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively com- bines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of 1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, 2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex loco- motive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps as well as climbing over high obstacles.
    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. 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