Prof. Dr. rer. nat. Ioannis Iossifidis

AUSGEWÄHLTE PUBLIKATIONEN

• anthropomorphic robot arm anthropomorphism arm movement model artificial skin attractor dynamics approach Autonomous robotics BCI behavior generation direct physical interaction Dynamical systems force sensing haptic interface human motor behavior human operators intuition human perception inverse kinematics Machine Learning man machine interaction man-machine-interaction manipulators mechanical structure modeling movement model object recognition operators gaze pervasive design robot manipulator control robot vision robotic assistant sensory channels user modelling

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  44 Einträge « ‹ 1 von 5 › »

  2022

  44.

  Doliwa, Sebastian; Hussain, Muhammad Ayaz; Sziburis, Tim; Iossifidis, Ioannis

  Biologically Inspired Model for Timed Motion in Robotic Systems Proceedings Article

  In: 9th IEEE RAS/EMBS International Conference on Biomedical Robotics & Biomechatronics, IEEE, Seoul, South Korea, 2022.

  BibTeX | Schlagwörter: Autonomous robotics, Dynamical systems

  @inproceedings{doliwaBiologicallyInspiredModel2022,
  
  title = {Biologically Inspired Model for Timed Motion in Robotic Systems},
  
  author = {Sebastian Doliwa and Muhammad Ayaz Hussain and Tim Sziburis and Ioannis Iossifidis},
  
  year  = {2022},
  
  date = {2022-08-12},
  
  urldate = {2022-08-12},
  
  booktitle = {9th IEEE RAS/EMBS International Conference on Biomedical Robotics & Biomechatronics},
  
  publisher = {IEEE},
  
  address = {Seoul, South Korea},
  
  keywords = {Autonomous robotics, Dynamical systems},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  2021

  43.

  Doliwa, Sebastian; Hussain, Muhammad Ayaz; Sziburis, Tim; Iossifidis, Ioannis

  Biologically Inspired Model for Timed Motion in Robotic Systems Artikel

  In: arXiv:2106.15864 [cs, math], 2021.

  Abstract | BibTeX | Schlagwörter: attractor dynamics approach, Autonomous robotics, Dynamical systems

  @article{doliwaBiologicallyInspiredModel2021,
  
  title = {Biologically Inspired Model for Timed Motion in Robotic Systems},
  
  author = {Sebastian Doliwa and Muhammad Ayaz Hussain and Tim Sziburis and Ioannis Iossifidis},
  
  year  = {2021},
  
  date = {2021-07-01},
  
  urldate = {2021-07-01},
  
  journal = {arXiv:2106.15864 [cs, math]},
  
  abstract = {The goal of this work is the development of a motion model for sequentially timed movement actions in robotic systems under specific consideration of temporal stabilization, that is maintaining an approximately constant overall movement time (isochronous behavior). This is demonstrated both in simulation and on a physical robotic system for the task of intercepting a moving target in three-dimensional space. Motivated from humanoid motion, timing plays a vital role to generate a naturalistic behavior in interaction with the dynamic environment as well as adaptively planning and executing action sequences on-line. In biological systems, many of the physiological and anatomical functions follow a particular level of periodicity and stabilization, which exhibit a certain extent of resilience against external disturbances. A main aspect thereof is stabilizing movement timing against limited perturbations. Especially human arm movement, namely when it is tasked to reach a certain goal point, pose or configuration, shows a stabilizing behavior. This work incorporates the utilization of an extended Kalman filter (EKF) which was implemented to predict the target position while coping with non-linear system dynamics. The periodicity and temporal stabilization in biological systems was artificially generated by a Hopf oscillator, yielding a sinusoidal velocity profile for smooth and repeatable motion.},
  
  keywords = {attractor dynamics approach, Autonomous robotics, Dynamical systems},
  
  pubstate = {published},
  
  tppubtype = {article}
  
  }
  
  

  Schließen

  The goal of this work is the development of a motion model for sequentially timed movement actions in robotic systems under specific consideration of temporal stabilization, that is maintaining an approximately constant overall movement time (isochronous behavior). This is demonstrated both in simulation and on a physical robotic system for the task of intercepting a moving target in three-dimensional space. Motivated from humanoid motion, timing plays a vital role to generate a naturalistic behavior in interaction with the dynamic environment as well as adaptively planning and executing action sequences on-line. In biological systems, many of the physiological and anatomical functions follow a particular level of periodicity and stabilization, which exhibit a certain extent of resilience against external disturbances. A main aspect thereof is stabilizing movement timing against limited perturbations. Especially human arm movement, namely when it is tasked to reach a certain goal point, pose or configuration, shows a stabilizing behavior. This work incorporates the utilization of an extended Kalman filter (EKF) which was implemented to predict the target position while coping with non-linear system dynamics. The periodicity and temporal stabilization in biological systems was artificially generated by a Hopf oscillator, yielding a sinusoidal velocity profile for smooth and repeatable motion.

  Schließen

  2018

  42.

  Hussain, Muhammad Ayaz; Klaes, Christian; Iossifidis, Ioannis

  Toward a Model of Timed Arm Movement Based on Temporal Tuning of Neurons in Primary Motor (MI) and Posterior Parietal Cortex (PPC) Title Proceedings Article

  In: BC18 : Computational Neuroscience & Neurotechnology Bernstein Conference 2018, BCCN, 2018.

  Abstract | BibTeX | Schlagwörter: Autonomous robotics, Dynamical systems, movement model

  @inproceedings{bccn18,
  
  title = {Toward a Model of Timed Arm Movement Based on Temporal Tuning of Neurons in Primary Motor (MI) and Posterior Parietal Cortex (PPC) Title},
  
  author = {Muhammad Ayaz Hussain and Christian Klaes and Ioannis Iossifidis},
  
  year  = {2018},
  
  date = {2018-01-01},
  
  urldate = {2018-01-01},
  
  booktitle = {BC18 : Computational Neuroscience & Neurotechnology Bernstein Conference 2018},
  
  publisher = {BCCN},
  
  abstract = {To study driver behavior we set up a lab with fixed base driving simulators. In order to compensate for the lack of physical feedback in this scenario, we aimed for another means of increasing the realism of our system. In the following, we propose an efficient method of head tracking and its integration in our driving simulation. Furthermore, we illuminate why this is a promising boost of the subjects immersion in the virtual world. Our idea for increasing the feeling of immersion is to give the subject feedback on head movements relative to the screen. A real driver sometimes moves his head in order to see something better or to look behind an occluding object. In addition to these intentional movements, a study conducted by Zirkovitz and Harris has revealed that drivers involuntarily tilt their heads when they go around corners in order to maximize the use of visual information available in the scene. Our system reflects the visual changes of any head movement and hence gives feedback on both involuntary and intentional motion. If, for example, subjects move to the left, they will see more from the right-hand side of the scene. If, on the other hand, they move upwards, a larger fraction of the engine hood will be visible. The same holds for the rear view mirror},
  
  keywords = {Autonomous robotics, Dynamical systems, movement model},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  To study driver behavior we set up a lab with fixed base driving simulators. In order to compensate for the lack of physical feedback in this scenario, we aimed for another means of increasing the realism of our system. In the following, we propose an efficient method of head tracking and its integration in our driving simulation. Furthermore, we illuminate why this is a promising boost of the subjects immersion in the virtual world. Our idea for increasing the feeling of immersion is to give the subject feedback on head movements relative to the screen. A real driver sometimes moves his head in order to see something better or to look behind an occluding object. In addition to these intentional movements, a study conducted by Zirkovitz and Harris has revealed that drivers involuntarily tilt their heads when they go around corners in order to maximize the use of visual information available in the scene. Our system reflects the visual changes of any head movement and hence gives feedback on both involuntary and intentional motion. If, for example, subjects move to the left, they will see more from the right-hand side of the scene. If, on the other hand, they move upwards, a larger fraction of the engine hood will be visible. The same holds for the rear view mirror

  Schließen

  2017

  41.

  Iossifidis, Ioannis; Hussain, Muhammad Ayaz; Klaes, Christian

  Temporal stabilized arm movement for efficient neuroprosthetic control by individuals with tetraplegia Sonstige

  2017.

  Abstract | BibTeX | Schlagwörter: Autonomous robotics, Dynamical systems, movement model, neuroprosthetic

  @misc{Iossifidis2017a,
  
  title = {Temporal stabilized arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
  
  author = {Ioannis Iossifidis and Muhammad Ayaz Hussain and Christian Klaes},
  
  year  = {2017},
  
  date = {2017-01-01},
  
  urldate = {2017-01-01},
  
  publisher = {SfN 2017},
  
  abstract = {The generation of discrete movement with distinct and stable time courses characterizes each human movement and reflect the need to perform catching and interception tasks and for timed action sequences, incorporating dynamically changing environmental constraints. Several lines of evidence suggest neuronal mechanism for the initiation of movements i.e. in the supplementary motor area (SMA) and the premotor cortex and for movement planning mechanism generating velocity profiles satisfying time constraints. In order to meet the requirements of on-line evolving trajectories we propose a model, based on dynamical systems which describes goal directed trajectories in humans and generates trajectories for redundant anthropomorphic robotic arms The current study aim to evaluate the temporal characteristics of primary motor and posterior parietal cortex in patients with tetraplegia by using inception task implemented in virtual reality. The participants will be implanted with two 96-channel intracortical microelectrode arrays in the Primary Motor and Post Parietal Cortex. In the training phase the participants will be confronted with the observation of a robotic arm intercepting the bob of a pendulum at the lowest point of it's trajectory (maximum velocity) - the end effector reaches at the same time as the bob of the pendulum the lowest point of the trajectory performing a perfectly timed movement. The arm is positioned perpendicular to the oscillation plane exactly at the hight of the interception point to generate a one dimensional trajectory to the target. The time to contact between the robot's end effector and the bob of the pendulum is maintained constant and during the different sessions the distance between end effector and the point of interception is gradually increased. In order to catch up and to reach in time, either velocity formation or initiation time of the movement have to be changed. Both effects will be investigated independently. For the decoding of movement-related information we introduce a framework exploiting a deep learning approach with a convolutional neural networks.},
  
  keywords = {Autonomous robotics, Dynamical systems, movement model, neuroprosthetic},
  
  pubstate = {published},
  
  tppubtype = {misc}
  
  }
  
  

  Schließen

  The generation of discrete movement with distinct and stable time courses characterizes each human movement and reflect the need to perform catching and interception tasks and for timed action sequences, incorporating dynamically changing environmental constraints. Several lines of evidence suggest neuronal mechanism for the initiation of movements i.e. in the supplementary motor area (SMA) and the premotor cortex and for movement planning mechanism generating velocity profiles satisfying time constraints. In order to meet the requirements of on-line evolving trajectories we propose a model, based on dynamical systems which describes goal directed trajectories in humans and generates trajectories for redundant anthropomorphic robotic arms The current study aim to evaluate the temporal characteristics of primary motor and posterior parietal cortex in patients with tetraplegia by using inception task implemented in virtual reality. The participants will be implanted with two 96-channel intracortical microelectrode arrays in the Primary Motor and Post Parietal Cortex. In the training phase the participants will be confronted with the observation of a robotic arm intercepting the bob of a pendulum at the lowest point of it's trajectory (maximum velocity) - the end effector reaches at the same time as the bob of the pendulum the lowest point of the trajectory performing a perfectly timed movement. The arm is positioned perpendicular to the oscillation plane exactly at the hight of the interception point to generate a one dimensional trajectory to the target. The time to contact between the robot's end effector and the bob of the pendulum is maintained constant and during the different sessions the distance between end effector and the point of interception is gradually increased. In order to catch up and to reach in time, either velocity formation or initiation time of the movement have to be changed. Both effects will be investigated independently. For the decoding of movement-related information we introduce a framework exploiting a deep learning approach with a convolutional neural networks.

  Schließen

  40.

  Iossifidis, Ioannis; Klaes, Christian

  Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia Sonstige

  2017.

  Abstract | BibTeX | Schlagwörter: Autonomous robotics, BCI, Dynamical systems, movement model, neuroprosthetic

  @misc{Iossifidis2017b,
  
  title = {Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
  
  author = {Ioannis Iossifidis and Christian Klaes},
  
  year  = {2017},
  
  date = {2017-01-01},
  
  urldate = {2017-01-01},
  
  publisher = {SfN 2017},
  
  abstract = {Over the last decades the generation mechanism and the representation of goal- directed movements has been a topic of intensive neurophysiological research. The investigation in the motor, premotor, and parietal areas led to the discovery that the direction of hand's movement in space was encoded by populations of neurons in these areas together with many other movement parameters. These distributions of population activation reflect how movements are prepared ahead of movement initiation, as revealed by activity induced by cues that precede the imperative signal (Georgopoulos, 1991). Inspired by those findings a model based on dynamical systems was proposed both, to model goal directed trajectories in humans and to generate trajectories for redundant anthropomorphic robotic arms. The analysis of the attractor dynamics based on the qualitative comparison with measurements of resulting trajectories taken from arm movement experiments with humans (Grimme u. a., 2012) created a framework able to reproduce and to generate naturalistic human like arm trajectories (Iossifidis und Rano, 2013; Iossifidis, Schöner u. a., 2006). The main idea of the methodology is to choose low-dimensional, behavioral va- riables of the goal task can be represented as attractor states of those variables. The movement is generated through a dynamical system with attractors and repellers on the behavioral space, at the goal and constraint positions respectively. When the motion of the robot evolves according to the dynamics of these systems, the behavioral variables will be stabilized at their attractors. Movement is represented by the polar coordinates $phi$,$theta$ of the movement direction (heading direction) and the angular frequency $ømega$ of a hopf oscillator, generating the velocity profile of the arm movement. Therefore, the system dynamics will be expressed in terms of these variables. The target and each obstacle induce vector fields over these variables in a way that states where the hand is moving closer to the target are attractive, while states where it is moving towards an obstacle are repellant. Contributions from different sources are weighted by different factors, e.g. in the vicinity of an obstacle, the contribution from that obstacle must dominate the behavior to guarantee constraint satisfaction (collision prevention). Based on three parameters the presented framework is able to generate temporal stabilized (timed) discrete movements, dealing with disturbances and maintaining an approximately constant movement time. In the current study we will implant two 96-channel intracortical microelectrode arrays in the primary motor and the posterior parietal cortex (PPC) of an individual with tetraplegia. In the training phase the parameters of the dynamical systems will be tuned and optimized by machine learning algorithms. Rather controlling directly the arm movement and adjusting continuously parameters, the patient adjust by his or hers thoughts the three parameters of the dynamics, which remain almost constant during the movement. Only when the motion plan is changing the parameters have to be readjusted. The target directed trajectory evolves from the attractor solution of the dynamical systems equations, which means that the trajectory is generated while the system is in a stable stationary state, a fixed-point attractor. The increase of the degree of assistance lowers the cognitive load of the patient and enables the acknowledgement of the desired task without frustration. In addition we aim to replace the robotic manipulator by an exoskeleton for the upper body which will enable the patients to move his or hers own limbs, which would complete the development of a real neuroprosthetic device for every day use.},
  
  keywords = {Autonomous robotics, BCI, Dynamical systems, movement model, neuroprosthetic},
  
  pubstate = {published},
  
  tppubtype = {misc}
  
  }
  
  

  Schließen

  Over the last decades the generation mechanism and the representation of goal- directed movements has been a topic of intensive neurophysiological research. The investigation in the motor, premotor, and parietal areas led to the discovery that the direction of hand's movement in space was encoded by populations of neurons in these areas together with many other movement parameters. These distributions of population activation reflect how movements are prepared ahead of movement initiation, as revealed by activity induced by cues that precede the imperative signal (Georgopoulos, 1991). Inspired by those findings a model based on dynamical systems was proposed both, to model goal directed trajectories in humans and to generate trajectories for redundant anthropomorphic robotic arms. The analysis of the attractor dynamics based on the qualitative comparison with measurements of resulting trajectories taken from arm movement experiments with humans (Grimme u. a., 2012) created a framework able to reproduce and to generate naturalistic human like arm trajectories (Iossifidis und Rano, 2013; Iossifidis, Schöner u. a., 2006). The main idea of the methodology is to choose low-dimensional, behavioral va- riables of the goal task can be represented as attractor states of those variables. The movement is generated through a dynamical system with attractors and repellers on the behavioral space, at the goal and constraint positions respectively. When the motion of the robot evolves according to the dynamics of these systems, the behavioral variables will be stabilized at their attractors. Movement is represented by the polar coordinates $phi$,$theta$ of the movement direction (heading direction) and the angular frequency $ømega$ of a hopf oscillator, generating the velocity profile of the arm movement. Therefore, the system dynamics will be expressed in terms of these variables. The target and each obstacle induce vector fields over these variables in a way that states where the hand is moving closer to the target are attractive, while states where it is moving towards an obstacle are repellant. Contributions from different sources are weighted by different factors, e.g. in the vicinity of an obstacle, the contribution from that obstacle must dominate the behavior to guarantee constraint satisfaction (collision prevention). Based on three parameters the presented framework is able to generate temporal stabilized (timed) discrete movements, dealing with disturbances and maintaining an approximately constant movement time. In the current study we will implant two 96-channel intracortical microelectrode arrays in the primary motor and the posterior parietal cortex (PPC) of an individual with tetraplegia. In the training phase the parameters of the dynamical systems will be tuned and optimized by machine learning algorithms. Rather controlling directly the arm movement and adjusting continuously parameters, the patient adjust by his or hers thoughts the three parameters of the dynamics, which remain almost constant during the movement. Only when the motion plan is changing the parameters have to be readjusted. The target directed trajectory evolves from the attractor solution of the dynamical systems equations, which means that the trajectory is generated while the system is in a stable stationary state, a fixed-point attractor. The increase of the degree of assistance lowers the cognitive load of the patient and enables the acknowledgement of the desired task without frustration. In addition we aim to replace the robotic manipulator by an exoskeleton for the upper body which will enable the patients to move his or hers own limbs, which would complete the development of a real neuroprosthetic device for every day use.

  Schließen

  39.

  Klaes, Christian; Iossifidis, Ioannis

  Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia Konferenz

  SfN Meeting 2017, 2017.

  BibTeX | Schlagwörter: Autonomous robotics, BCI, Dynamical systems, movement model, neuroprosthetic

  @conference{nokey,
  
  title = {Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
  
  author = {Christian Klaes and Ioannis Iossifidis},
  
  year  = {2017},
  
  date = {2017-01-01},
  
  urldate = {2017-01-01},
  
  booktitle = {SfN Meeting 2017},
  
  keywords = {Autonomous robotics, BCI, Dynamical systems, movement model, neuroprosthetic},
  
  pubstate = {published},
  
  tppubtype = {conference}
  
  }
  
  

  Schließen

  2013

  38.

  Rano, Inaki; Iossifidis, Ioannis

  Modelling human arm motion through the attractor dynamics approach Proceedings Article

  In: 2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013, S. 2088–2093, 2013, ISBN: 9781479927449.

  Abstract | Links | BibTeX | Schlagwörter: arm movement model, Autonomous robotics, Dynamical systems, movement model

  @inproceedings{Rano2013,
  
  title = {Modelling human arm motion through the attractor dynamics approach},
  
  author = {Inaki Rano and Ioannis Iossifidis},
  
  doi = {10.1109/ROBIO.2013.6739777},
  
  isbn = {9781479927449},
  
  year  = {2013},
  
  date = {2013-01-01},
  
  booktitle = {2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013},
  
  pages = {2088--2093},
  
  abstract = {Movement generation in robotics is an old problem with many excellent solutions. Most of them, however, look for optimality according to some metrics, but have no biological inspiration or cannot be used to imitate biological motion. For a human these techniques behave in a non-naturalistic way. This poses a problem for instance in human-robot interaction and, in general, for a good acceptance of robots in society. The present work presents a new analysis of the attractor dynamics approach to movement generation used in an anthropomorphic robot arm. Our analysis points to the possibility of using this approach to generate human-like arm trajectories in robots. One key property of human trajectories in pick-and-place tasks is the planarity of the trajectory of the end effector in 3D space. We show that this feature is also displayed by the attractor dynamic approach and, therefore, is a good candidate to the generation of naturalistic arm movements. textcopyright 2013 IEEE.},
  
  keywords = {arm movement model, Autonomous robotics, Dynamical systems, movement model},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  Movement generation in robotics is an old problem with many excellent solutions. Most of them, however, look for optimality according to some metrics, but have no biological inspiration or cannot be used to imitate biological motion. For a human these techniques behave in a non-naturalistic way. This poses a problem for instance in human-robot interaction and, in general, for a good acceptance of robots in society. The present work presents a new analysis of the attractor dynamics approach to movement generation used in an anthropomorphic robot arm. Our analysis points to the possibility of using this approach to generate human-like arm trajectories in robots. One key property of human trajectories in pick-and-place tasks is the planarity of the trajectory of the end effector in 3D space. We show that this feature is also displayed by the attractor dynamic approach and, therefore, is a good candidate to the generation of naturalistic arm movements. textcopyright 2013 IEEE.

  Schließen

  • doi:10.1109/ROBIO.2013.6739777

  Schließen

  37.

  Iossifidis, Ioannis; Rano, Ianki

  Modeling Human Arm Motion by Means of Attractor Dynamics Approach Proceedings Article

  In: Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2013), 2013.

  Abstract | BibTeX | Schlagwörter: arm movement model, Autonomous robotics, Dynamical systems, movement model

  @inproceedings{Iossifidis2013a,
  
  title = {Modeling Human Arm Motion by Means of Attractor Dynamics Approach},
  
  author = {Ioannis Iossifidis and Ianki Rano},
  
  year  = {2013},
  
  date = {2013-01-01},
  
  booktitle = {Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2013)},
  
  abstract = {Autonomous robots with limited computational capacity call for control approaches that generate meaningful, goal-directed behavior without using a large amount of resources. The attractor dynamics approach to movement generation is a framework that links sensor data to motor commands via coupled dynamical systems that have attractors at behaviorally desired states. The low computational demands leave enough system resources for higher level function like forming a sequence of local goals to reach a distant one. The comparatively high performance of local behavior generation allows the global planning to be relatively simple. 
  
   
  
   In the present paper, we apply this approach to generate walking trajectories for a small humanoid robot, the Aldebaran Nao, that are goal-directed and avoid obstacles. The sensor information is a single camera in the head of the robot. The limited field of vision is compensated by head movements. The design of the dynamical system for motion generation and the choice of state variable makes a computationally expensive scene representation or local map building unnecessary.},
  
  keywords = {arm movement model, Autonomous robotics, Dynamical systems, movement model},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  Autonomous robots with limited computational capacity call for control approaches that generate meaningful, goal-directed behavior without using a large amount of resources. The attractor dynamics approach to movement generation is a framework that links sensor data to motor commands via coupled dynamical systems that have attractors at behaviorally desired states. The low computational demands leave enough system resources for higher level function like forming a sequence of local goals to reach a distant one. The comparatively high performance of local behavior generation allows the global planning to be relatively simple.
  
  In the present paper, we apply this approach to generate walking trajectories for a small humanoid robot, the Aldebaran Nao, that are goal-directed and avoid obstacles. The sensor information is a single camera in the head of the robot. The limited field of vision is compensated by head movements. The design of the dynamical system for motion generation and the choice of state variable makes a computationally expensive scene representation or local map building unnecessary.

  Schließen

  2012

  36.

  Iossifidis, Ioannis

  Sequence Generation for Grasping Tasks by Means of Dynamical Systems Konferenz

  BC12 : Computational Neuroscience $backslash$& Neurotechnology Bernstein Conference $backslash$& Neurex Annual Meeting 2012, 2012.

  BibTeX | Schlagwörter: Autonomous robotics, Dynamical systems, grasping, sequence generation

  @conference{Iossifidis2012,
  
  title = {Sequence Generation for Grasping Tasks by Means of Dynamical Systems},
  
  author = {Ioannis Iossifidis},
  
  year  = {2012},
  
  date = {2012-01-01},
  
  booktitle = {BC12 : Computational Neuroscience $backslash$& Neurotechnology Bernstein Conference $backslash$& Neurex Annual Meeting 2012},
  
  keywords = {Autonomous robotics, Dynamical systems, grasping, sequence generation},
  
  pubstate = {published},
  
  tppubtype = {conference}
  
  }
  
  

  Schließen

  2011

  35.

  Zibner, S K U; Faubel, Christian; Iossifidis, Ioannis; Schöner, G

  Dynamic neural fields as building blocks of a cortex-inspired architecture for robotic scene representation Artikel

  In: IEEE Transactions on Autonomous Mental Development, Bd. 3, Nr. 1, 2011, ISSN: 19430604.

  Abstract | Links | BibTeX | Schlagwörter: Autonomous robotics, dynamic field theory (DFT), Dynamical systems, embodied cognition, neural processing

  @article{Zibner2011,
  
  title = {Dynamic neural fields as building blocks of a cortex-inspired architecture for robotic scene representation},
  
  author = {S K U Zibner and Christian Faubel and Ioannis Iossifidis and G Schöner},
  
  doi = {10.1109/TAMD.2011.2109714},
  
  issn = {19430604},
  
  year  = {2011},
  
  date = {2011-01-01},
  
  urldate = {2011-01-01},
  
  journal = {IEEE Transactions on Autonomous Mental Development},
  
  volume = {3},
  
  number = {1},
  
  abstract = {Based on the concepts of dynamic field theory (DFT), we present an architecture that autonomously generates scene representations by controlling gaze and attention, creating visual objects in the foreground, tracking objects, reading them into working memory, and taking into account their visibility. At the core of this architecture are three-dimensional dynamic neural fields (DNFs) that link feature to spatial information. These three-dimensional fields couple into lower dimensional fields, which provide the links to the sensory surface and to the motor systems. We discuss how DNFs can be used as building blocks for cognitive architectures, characterize the critical bifurcations in DNFs, as well as the possible coupling structures among DNFs. In a series of robotic experiments, we demonstrate how the DNF architecture provides the core functionalities of a scene representation. textcopyright 2011 IEEE.},
  
  keywords = {Autonomous robotics, dynamic field theory (DFT), Dynamical systems, embodied cognition, neural processing},
  
  pubstate = {published},
  
  tppubtype = {article}
  
  }
  
  

  Schließen

  Based on the concepts of dynamic field theory (DFT), we present an architecture that autonomously generates scene representations by controlling gaze and attention, creating visual objects in the foreground, tracking objects, reading them into working memory, and taking into account their visibility. At the core of this architecture are three-dimensional dynamic neural fields (DNFs) that link feature to spatial information. These three-dimensional fields couple into lower dimensional fields, which provide the links to the sensory surface and to the motor systems. We discuss how DNFs can be used as building blocks for cognitive architectures, characterize the critical bifurcations in DNFs, as well as the possible coupling structures among DNFs. In a series of robotic experiments, we demonstrate how the DNF architecture provides the core functionalities of a scene representation. textcopyright 2011 IEEE.

  Schließen

  • doi:10.1109/TAMD.2011.2109714

  Schließen

  44 Einträge « ‹ 1 von 5 › »