2022
Sebastian Doliwa; Muhammad Ayaz Hussain; Tim Sziburis; Ioannis Iossifidis
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 | Tags: 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}
}
2021
Sebastian Doliwa; Muhammad Ayaz Hussain; Tim Sziburis; Ioannis Iossifidis
Biologically Inspired Model for Timed Motion in Robotic Systems Journal Article
In: arXiv:2106.15864 [cs, math], 2021.
Abstract | BibTeX | Tags: 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}
}
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.
2018
Muhammad Ayaz Hussain; Christian Klaes; Ioannis Iossifidis:
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 | Tags: BCI, dynamical systems, Machine Learning, 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 = {BCI, dynamical systems, Machine Learning, movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
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
2017
Ioannis Iossifidis:; Muhammad Ayaz Hussain; Christian Klaes
Temporal stabilized arm movement for efficient neuroprosthetic control by individuals with tetraplegia Miscellaneous
2017.
Abstract | BibTeX | Tags: 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},
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}
}
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.
Ioannis Iossifidis; C Klaes
Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia Miscellaneous
2017.
Abstract | BibTeX | Tags: 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 C 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 = {BCI, dynamical systems, movement model, neuroprosthetic},
pubstate = {published},
tppubtype = {misc}
}
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.
Klaes C I. Iossifidis
Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia Conference
SfN Meeting 2017, 2017.
BibTeX | Tags: 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 = {Klaes C I. Iossifidis},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {SfN Meeting 2017},
keywords = {BCI, dynamical systems, movement model, neuroprosthetic},
pubstate = {published},
tppubtype = {conference}
}
2013
Inaki Rano; Ioannis Iossifidis
Modelling human arm motion through the attractor dynamics approach Proceedings Article
In: 2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013, pp. 2088–2093, 2013, ISBN: 9781479927449.
Abstract | Links | BibTeX | Tags: 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 = {dynamical systems, movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Ioannis Iossifidis; Ianki Rano
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 | Tags: 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 = {dynamical systems, movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
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.
2012
Ioannis Iossifidis
Sequence Generation for Grasping Tasks by Means of Dynamical Systems Conference
BC12 : Computational Neuroscience $backslash$& Neurotechnology Bernstein Conference $backslash$& Neurex Annual Meeting 2012, 2012.
BibTeX | Tags: 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}
}
2011
S K U Zibner; C Faubel; I Iossifidis; G Schöner
Dynamic neural fields as building blocks of a cortex-inspired architecture for robotic scene representation Journal Article
In: IEEE Transactions on Autonomous Mental Development, vol. 3, no. 1, 2011, ISSN: 19430604.
Abstract | Links | BibTeX | Tags: 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 C Faubel and I Iossifidis and G Schöner},
doi = {10.1109/TAMD.2011.2109714},
issn = {19430604},
year = {2011},
date = {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}
}
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.
Hendrik Reimann; Ioannis Iossifidis; Gregor Schöner
Autonomous movement generation for manipulators with multiple simultaneous constraints using the attractor dynamics approach Proceedings Article
In: 2011 IEEE International Conference on Robotics and Automation, ICRA2011, 2011.
Abstract | BibTeX | Tags: anthropomorphic robot arm, attractor dynamics approach, dynamical systems
@inproceedings{Reimann2011,
title = {Autonomous movement generation for manipulators with multiple simultaneous constraints using the attractor dynamics approach},
author = {Hendrik Reimann and Ioannis Iossifidis and Gregor Schöner},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {2011 IEEE International Conference on Robotics and Automation, ICRA2011},
abstract = {The movement of autonomous agents in natural environments is restricted by potentially large numbers of con- straints. To generate behavior that fulfills all given constraints simultaneously, the attractor dynamics approach to movement generation represents each constraint by a dynamical system with attractors or repellors at desired or undesired values of a relevant variable. These dynamical systems are transformed into vector fields over the control variables of a robotic agent that force the state of the whole system in directions beneficial to the satisfaction of the behavioral constraint. The attractor dynamics approach was recently successfully applied to the generation of manipulator motion trajectories avoiding collision with obstacles [1] and constraints on gripper orientation during reaching and grasping movements [2]. Continuing that body of work, this paper proposes a system which generates movements satisfying both obstacle avoidance and gripper orientation constraints simultaneously. As an extension, the additional constraint of avoiding hardware limits for joint angles is in- cluded. Properties of the resulting system are demonstrated by a systematic study generating movements with a large number of constraints in different scene setups. Specific characteristics are highlighted by several showcase example movements.},
keywords = {anthropomorphic robot arm, attractor dynamics approach, dynamical systems},
pubstate = {published},
tppubtype = {inproceedings}
}
The movement of autonomous agents in natural environments is restricted by potentially large numbers of con- straints. To generate behavior that fulfills all given constraints simultaneously, the attractor dynamics approach to movement generation represents each constraint by a dynamical system with attractors or repellors at desired or undesired values of a relevant variable. These dynamical systems are transformed into vector fields over the control variables of a robotic agent that force the state of the whole system in directions beneficial to the satisfaction of the behavioral constraint. The attractor dynamics approach was recently successfully applied to the generation of manipulator motion trajectories avoiding collision with obstacles [1] and constraints on gripper orientation during reaching and grasping movements [2]. Continuing that body of work, this paper proposes a system which generates movements satisfying both obstacle avoidance and gripper orientation constraints simultaneously. As an extension, the additional constraint of avoiding hardware limits for joint angles is in- cluded. Properties of the resulting system are demonstrated by a systematic study generating movements with a large number of constraints in different scene setups. Specific characteristics are highlighted by several showcase example movements.
Ioannis Iossifidis; Darius Malysiak; Hendrik Reimann
Model-free local navigation for humanoid robots Proceedings Article
In: Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011), 2011.
Abstract | BibTeX | Tags: attractor dynamics approach, dynamical systems
@inproceedings{Iossifidis2011A,
title = {Model-free local navigation for humanoid robots},
author = {Ioannis Iossifidis and Darius Malysiak and Hendrik Reimann},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011)},
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 = {attractor dynamics approach, dynamical systems},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
D Malysiak; H Reiman; I Iossifidis
Human like trajectories for humanoid robots Conference
BC11 : Computational Neuroscience $backslash$& Neurotechnology Bernstein Conference $backslash$& Neurex Annual Meeting 2011, 2011.
BibTeX | Tags: attractor dynamics approach, dynamical systems
@conference{Malysiak2011,
title = {Human like trajectories for humanoid robots},
author = {D Malysiak and H Reiman and I Iossifidis},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {BC11 : Computational Neuroscience $backslash$& Neurotechnology Bernstein Conference $backslash$& Neurex Annual Meeting 2011},
keywords = {attractor dynamics approach, dynamical systems},
pubstate = {published},
tppubtype = {conference}
}
2010
Hendrik Reimann; Ioannis Iossifidis; Gregor Schoner; Gregor Schöner
Integrating orientation constraints into the attractor dynamics approach for autonomous manipulation Proceedings Article
In: 2010 10th IEEE-RAS International Conference on Humanoid Robots, pp. 294–301, IEEE, 2010, ISBN: 978-1-4244-8688-5.
Abstract | Links | BibTeX | Tags: attractor dynamics approach, Autonomous robotics, dynamical systems, Inverse kinematics
@inproceedings{Reimann2010a,
title = {Integrating orientation constraints into the attractor dynamics approach for autonomous manipulation},
author = {Hendrik Reimann and Ioannis Iossifidis and Gregor Schoner and Gregor Schöner},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5686349},
doi = {10.1109/ICHR.2010.5686349},
isbn = {978-1-4244-8688-5},
year = {2010},
date = {2010-12-01},
urldate = {2010-12-01},
booktitle = {2010 10th IEEE-RAS International Conference on Humanoid Robots},
pages = {294–301},
publisher = {IEEE},
abstract = {When autonomous robots generate behavior in complex environments they must satisfy multiple different constraints such as moving toward a target, avoidance of obstacles, or alignment of the gripper with a particular orientation. It is often convenient to represent each type of constraint in a specific reference frame, so that the satisfaction of all constraints requires transformation into a shared base frame. In the attractor dynamics approach, behavior is generated as an attractor solution of a dynamical system that is formulated in such a base frame to enable control. Each constraint contributes an attractive (for targets) or repulsive (for obstacles) component to the vector field. Here we show how these dynamic contributions can be formulated in different reference frames suited to each constraint and then be transformed and integrated within the base frame. Building on earlier work, we show how the orientation of the gripper can be integrated with other constraints on the movement of the manipulator. We also show, how an attractor dynamics of “neural” activation variables can be designed that activates and deactivates the different contributions to the vector field over time to generate a sequence of component movements. As a demonstration, we treat a manipulation task in which grasping oblong cylindrical objects is decomposed into an ensemble of separate constraints that are integrated and resolved using the attractor dynamics approach. The system is implemented on the small humanoid robot Nao, and illustrated in two exemplary movement tasks.},
keywords = {attractor dynamics approach, Autonomous robotics, dynamical systems, Inverse kinematics},
pubstate = {published},
tppubtype = {inproceedings}
}
When autonomous robots generate behavior in complex environments they must satisfy multiple different constraints such as moving toward a target, avoidance of obstacles, or alignment of the gripper with a particular orientation. It is often convenient to represent each type of constraint in a specific reference frame, so that the satisfaction of all constraints requires transformation into a shared base frame. In the attractor dynamics approach, behavior is generated as an attractor solution of a dynamical system that is formulated in such a base frame to enable control. Each constraint contributes an attractive (for targets) or repulsive (for obstacles) component to the vector field. Here we show how these dynamic contributions can be formulated in different reference frames suited to each constraint and then be transformed and integrated within the base frame. Building on earlier work, we show how the orientation of the gripper can be integrated with other constraints on the movement of the manipulator. We also show, how an attractor dynamics of “neural” activation variables can be designed that activates and deactivates the different contributions to the vector field over time to generate a sequence of component movements. As a demonstration, we treat a manipulation task in which grasping oblong cylindrical objects is decomposed into an ensemble of separate constraints that are integrated and resolved using the attractor dynamics approach. The system is implemented on the small humanoid robot Nao, and illustrated in two exemplary movement tasks.
H Reimann; I Iossifidis; G Schöner
Generating collision free reaching movements for redundant manipulators using dynamical systems Proceedings Article
In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5372–5379, IEEE, 2010, ISBN: 978-1-4244-6674-0.
Abstract | Links | BibTeX | Tags: attractor dynamics approach, central nervous system, collision avoidance, dynamical systems, manipulator dynamics, obstacle avoidance, redundant robot arm
@inproceedings{Reimann2010b,
title = {Generating collision free reaching movements for redundant manipulators using dynamical systems},
author = {H Reimann and I Iossifidis and G Schöner},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5650603},
doi = {10.1109/IROS.2010.5650603},
isbn = {978-1-4244-6674-0},
year = {2010},
date = {2010-10-01},
booktitle = {2010 IEEE/RSJ International Conference on Intelligent Robots and Systems},
pages = {5372–5379},
publisher = {IEEE},
abstract = {For autonomous robots to manipulate objects in unknown environments, they must be able to move their arms without colliding with nearby objects, other agents or humans. The simultaneous avoidance of multiple obstacles in real time by all link segments of a manipulator is still a hard task both in practice and in theory. We present a systematic scheme for the generation of collision free movements for redundant manipulators in scenes with arbitrarily many obstacles. Based on the dynamical systems approach to robotics, constraints are formulated as contributions to a dynamical system that erect attractors for targets and repellors for obstacles. These contributions are formulated in terms of variables relevant to each constraint and then transformed into vector fields over the manipulator joint velocity vector as an embedding space in which all constraints are simultaneously observed. We demonstrate the feasibility of the approach by implementing it on a real anthropomorphic 8-degrees-of-freedom redundant manipulator. In addition, performance is characterized by detecting failures in a systematic simulation experiment in randomized scenes with varying numbers of obstacles.},
keywords = {attractor dynamics approach, central nervous system, collision avoidance, dynamical systems, manipulator dynamics, obstacle avoidance, redundant robot arm},
pubstate = {published},
tppubtype = {inproceedings}
}
For autonomous robots to manipulate objects in unknown environments, they must be able to move their arms without colliding with nearby objects, other agents or humans. The simultaneous avoidance of multiple obstacles in real time by all link segments of a manipulator is still a hard task both in practice and in theory. We present a systematic scheme for the generation of collision free movements for redundant manipulators in scenes with arbitrarily many obstacles. Based on the dynamical systems approach to robotics, constraints are formulated as contributions to a dynamical system that erect attractors for targets and repellors for obstacles. These contributions are formulated in terms of variables relevant to each constraint and then transformed into vector fields over the manipulator joint velocity vector as an embedding space in which all constraints are simultaneously observed. We demonstrate the feasibility of the approach by implementing it on a real anthropomorphic 8-degrees-of-freedom redundant manipulator. In addition, performance is characterized by detecting failures in a systematic simulation experiment in randomized scenes with varying numbers of obstacles.
S K U Zibner; C Faubel; I Iossifidis; G Schöner; J P Spencer
Scenes and tracking with dynamic neural fields: How to update a robotic scene representation Proceedings Article
In: 2010 IEEE 9th International Conference on Development and Learning, ICDL-2010 - Conference Program, 2010, ISBN: 9781424469024.
Abstract | Links | BibTeX | Tags: Autonomous robotics, dynamic field theory (DFT), dynamical systems, embodied cognition, neural processing
@inproceedings{Zibner2010,
title = {Scenes and tracking with dynamic neural fields: How to update a robotic scene representation},
author = {S K U Zibner and C Faubel and I Iossifidis and G Schöner and J P Spencer},
doi = {10.1109/DEVLRN.2010.5578837},
isbn = {9781424469024},
year = {2010},
date = {2010-01-01},
booktitle = {2010 IEEE 9th International Conference on Development and Learning, ICDL-2010 - Conference Program},
abstract = {We present an architecture based on the Dynamic Field Theory for the problem of scene representation. At the core of this architecture are three-dimensional neural fields linking feature to spatial information. These three-dimensional fields are coupled to lower-dimensional fields that provide both a close link to the sensory surface and a close link to motor behavior. We highlight the updating mechanism of this architecture, both when a single object is selected and followed by the robot's head in smooth pursuit and in multi-item tracking when several items move simultaneously. textcopyright 2010 IEEE.},
keywords = {Autonomous robotics, dynamic field theory (DFT), dynamical systems, embodied cognition, neural processing},
pubstate = {published},
tppubtype = {inproceedings}
}
We present an architecture based on the Dynamic Field Theory for the problem of scene representation. At the core of this architecture are three-dimensional neural fields linking feature to spatial information. These three-dimensional fields are coupled to lower-dimensional fields that provide both a close link to the sensory surface and a close link to motor behavior. We highlight the updating mechanism of this architecture, both when a single object is selected and followed by the robot's head in smooth pursuit and in multi-item tracking when several items move simultaneously. textcopyright 2010 IEEE.
S K U Zibner; C Faubel; I Iossifidis; G Schöner
Scene representation for anthropomorphic robots: A dynamic neural field approach Proceedings Article
In: Joint 41st International Symposium on Robotics and 6th German Conference on Robotics 2010, ISR/ROBOTIK 2010, 2010, ISBN: 9781617387197.
Abstract | BibTeX | Tags: Autonomous robotics, dynamic field theory (DFT), dynamical systems, embodied cognition, neural processing
@inproceedings{Zibner2010b,
title = {Scene representation for anthropomorphic robots: A dynamic neural field approach},
author = {S K U Zibner and C Faubel and I Iossifidis and G Schöner},
isbn = {9781617387197},
year = {2010},
date = {2010-01-01},
booktitle = {Joint 41st International Symposium on Robotics and 6th German Conference on Robotics 2010, ISR/ROBOTIK 2010},
volume = {2},
abstract = {For autonomous robotic systems, the ability to represent a scene, to memorize and track objects and their associated features is a prerequisite for reasonable interactive behavior. In this paper, we present a biologically inspired architecture for scene representation that is based on Dynamic Field Theory. At the core of the architecture we make use of three-dimensional Dynamic Neural Fields for representing space-feature associations. These associations are built up autonomously in a sequential way and they are maintained and continuously updated. We demonstrate these capabilities in two experiments on an anthropomorphic robotic platform. In the first experiment we show the sequential scanning of a scene. The second experiment demonstrates the maintenance of associations for objects, which get out of view, and the correct update of the scene representation, if such objects are removed.},
keywords = {Autonomous robotics, dynamic field theory (DFT), dynamical systems, embodied cognition, neural processing},
pubstate = {published},
tppubtype = {inproceedings}
}
For autonomous robotic systems, the ability to represent a scene, to memorize and track objects and their associated features is a prerequisite for reasonable interactive behavior. In this paper, we present a biologically inspired architecture for scene representation that is based on Dynamic Field Theory. At the core of the architecture we make use of three-dimensional Dynamic Neural Fields for representing space-feature associations. These associations are built up autonomously in a sequential way and they are maintained and continuously updated. We demonstrate these capabilities in two experiments on an anthropomorphic robotic platform. In the first experiment we show the sequential scanning of a scene. The second experiment demonstrates the maintenance of associations for objects, which get out of view, and the correct update of the scene representation, if such objects are removed.
M A Grimm; I Iossifidis
Behavioral organization for mobile robotic systems: An attractor dynamics approach Proceedings Article
In: Joint 41st International Symposium on Robotics and 6th German Conference on Robotics 2010, ISR/ROBOTIK 2010, 2010, ISBN: 9781617387197.
Abstract | BibTeX | Tags: Autonomous robotics, behavior generation, dynamical systems, movement model
@inproceedings{Grimm2010a,
title = {Behavioral organization for mobile robotic systems: An attractor dynamics approach},
author = {M A Grimm and I Iossifidis},
isbn = {9781617387197},
year = {2010},
date = {2010-01-01},
booktitle = {Joint 41st International Symposium on Robotics and 6th German Conference on Robotics 2010, ISR/ROBOTIK 2010},
volume = {1},
abstract = {In this paper we describe an architecture for behavioral organization based on dynamical systems. This architecture enables the generation of complex behavioral sequences, which is demonstrated using the example of approaching and passing a door. The behavioral sequence is generated by activating and deactivating the elementary behaviors dependent on sensory information and internal logical conditions. The architecture is demonstrated on a mobile KOALA robot and in simulation as well.},
keywords = {Autonomous robotics, behavior generation, dynamical systems, movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we describe an architecture for behavioral organization based on dynamical systems. This architecture enables the generation of complex behavioral sequences, which is demonstrated using the example of approaching and passing a door. The behavioral sequence is generated by activating and deactivating the elementary behaviors dependent on sensory information and internal logical conditions. The architecture is demonstrated on a mobile KOALA robot and in simulation as well.
Hendrik Reimann; Ioannis Iossifidis; Gregor Schöner
End-effector obstacle avoidance using multiple dynamic variables Proceedings Article
In: ISR / ROBOTIK 2010, Munich, Germany, 2010.
Abstract | BibTeX | Tags: Autonomous robotics, behavior generation, dynamical systems, movement model
@inproceedings{Reimannd,
title = {End-effector obstacle avoidance using multiple dynamic variables},
author = {Hendrik Reimann and Ioannis Iossifidis and Gregor Schöner},
year = {2010},
date = {2010-01-01},
booktitle = {ISR / ROBOTIK 2010},
address = {Munich, Germany},
abstract = {The avoidance of obstacles is a crucial part of the generation of behavior for autonomos robotic agents. A standard method to produce trajectories to a given target that avoids a number of possibly mobile obstacles is the potential field approach introduced by Khatib, where an artificial potential field is constructed around target and obstacles, with the target acting as a global minimum and the obstacles as local maxima, the gradient of which is used to determine the (artificial) force acting on the robot at any moment. While the potential field approach has been used extensively for vehicle motion in a plane, applications for robotic manipulators suffer from a high level of complexity due to the formulation of constraints as forces necessitating the inclusion of dynamic properties of the manipulator into the system. We pursue a different solution to the problem of manipulator obstacle avoidance based on the dynamic approach to robotics, which states that all behavioral constraints for the generation of movement should be formulated as attractors or repellors of a dynamical systems. The problem of behavior design is thus separated from the control problem of how to realize the designed behavior, bringing the advantage of simplicity in the formulation of the former.},
keywords = {Autonomous robotics, behavior generation, dynamical systems, movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
The avoidance of obstacles is a crucial part of the generation of behavior for autonomos robotic agents. A standard method to produce trajectories to a given target that avoids a number of possibly mobile obstacles is the potential field approach introduced by Khatib, where an artificial potential field is constructed around target and obstacles, with the target acting as a global minimum and the obstacles as local maxima, the gradient of which is used to determine the (artificial) force acting on the robot at any moment. While the potential field approach has been used extensively for vehicle motion in a plane, applications for robotic manipulators suffer from a high level of complexity due to the formulation of constraints as forces necessitating the inclusion of dynamic properties of the manipulator into the system. We pursue a different solution to the problem of manipulator obstacle avoidance based on the dynamic approach to robotics, which states that all behavioral constraints for the generation of movement should be formulated as attractors or repellors of a dynamical systems. The problem of behavior design is thus separated from the control problem of how to realize the designed behavior, bringing the advantage of simplicity in the formulation of the former.
Matthias Grimm; Ioannis Iossifidis
Behavioral Organization for Mobile Robotic Systems: An Attractor Dynamics Approach Proceedings Article
In: ISR / ROBOTIK 2010, Munich, Germany, 2010.
Abstract | BibTeX | Tags: Autonomous robotics, behavior generation, dynamical systems, movement model, movile robot
@inproceedings{Grimm2010b,
title = {Behavioral Organization for Mobile Robotic Systems: An Attractor Dynamics Approach},
author = {Matthias Grimm and Ioannis Iossifidis},
year = {2010},
date = {2010-01-01},
booktitle = {ISR / ROBOTIK 2010},
address = {Munich, Germany},
abstract = {Autonomous systems generate different behaviors based on the perceived environmental situation. The organization of a set of behaviors plays an important role in the field of autonomous robotics. The organization architecture must be flexible, so that behavioral changes are possible if the sensory information changes. Furthermore, behavioral organization must be stable, so that small changes in sensory information do not lead to oscillations. To achieve this, all behaviors, but also the underlying organization architecture, are based on continuous dynamical systems. They are characterized by a set of dynamical variables, also referred to as state variables. These variables represent the activation or deactivation of a particular behavior. Elementary behaviors are dependent on the sensor input in a way, that changes of the sensorial information lead to qualitatively different behaviors. The so-called sensor context denotes whether a behavior is applicable in the current sensor situation or not. However, for complex systems consisting of many elementary behaviors, it is necessary to take logical conditions into account to generate a sequence of behaviors. Furthermore, some elementary behaviors can or even must run in parallel, while others exclude each other. This internal information requires knowledge about the logical interaction of the behaviors and is stored within binary matrices. This makes the overall organization structure very flexible and easy to extend. We present the architecture using the example of approaching and passing a door. The robot has to navigate from one room to another while simultaneously avoiding obstacles in its pathway.},
keywords = {Autonomous robotics, behavior generation, dynamical systems, movement model, movile robot},
pubstate = {published},
tppubtype = {inproceedings}
}
Autonomous systems generate different behaviors based on the perceived environmental situation. The organization of a set of behaviors plays an important role in the field of autonomous robotics. The organization architecture must be flexible, so that behavioral changes are possible if the sensory information changes. Furthermore, behavioral organization must be stable, so that small changes in sensory information do not lead to oscillations. To achieve this, all behaviors, but also the underlying organization architecture, are based on continuous dynamical systems. They are characterized by a set of dynamical variables, also referred to as state variables. These variables represent the activation or deactivation of a particular behavior. Elementary behaviors are dependent on the sensor input in a way, that changes of the sensorial information lead to qualitatively different behaviors. The so-called sensor context denotes whether a behavior is applicable in the current sensor situation or not. However, for complex systems consisting of many elementary behaviors, it is necessary to take logical conditions into account to generate a sequence of behaviors. Furthermore, some elementary behaviors can or even must run in parallel, while others exclude each other. This internal information requires knowledge about the logical interaction of the behaviors and is stored within binary matrices. This makes the overall organization structure very flexible and easy to extend. We present the architecture using the example of approaching and passing a door. The robot has to navigate from one room to another while simultaneously avoiding obstacles in its pathway.
Yulia Sandamirskaya; John Lipinski; Ioannis Iossifidis; G Schöner
Natural human-robot interaction through spatial language: a dynamic neural fields approach Proceedings Article
In: Proc. 19th IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2010), pp. 600–607, IEEE, 2010, ISSN: 1944-9445.
Links | BibTeX | Tags: Autonomous robotics, behavior generation, dynamical systems, man machine interaction, movement model, speech recognition
@inproceedings{Sandamirskayasubmitted,
title = {Natural human-robot interaction through spatial language: a dynamic neural fields approach},
author = {Yulia Sandamirskaya and John Lipinski and Ioannis Iossifidis and G Schöner},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5598671},
issn = {1944-9445},
year = {2010},
date = {2010-01-01},
booktitle = {Proc. 19th IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2010)},
pages = {600–607},
publisher = {IEEE},
keywords = {Autonomous robotics, behavior generation, dynamical systems, man machine interaction, movement model, speech recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
Stephan K U Zibner; Christian Faubel; Ioannis Iossifidis; Gregor Schöner
Scene Representation Based on Dynamic Field Theory: From Human to Machine Journal Article
In: Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience, 2010.
Links | BibTeX | Tags: dynamic neural field, dynamical systems, man machine interaction, scene representation, speech recognition
@article{Zibner2010a,
title = {Scene Representation Based on Dynamic Field Theory: From Human to Machine},
author = {Stephan K U Zibner and Christian Faubel and Ioannis Iossifidis and Gregor Schöner},
doi = {10.3389/conf.fncom.2010.51.00019},
year = {2010},
date = {2010-01-01},
journal = {Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience},
keywords = {dynamic neural field, dynamical systems, man machine interaction, scene representation, speech recognition},
pubstate = {published},
tppubtype = {article}
}
Stephan S K U Zibner; Christian Faubel; Ioannis Iossifidis; Gregor Schöner
Scene Representation for Anthropomorphic Robots: A Dynamic Neural Field Approach Proceedings Article
In: ISR / ROBOTIK 2010, VDE VERLAG GmbH, Munich, Germany, 2010.
Abstract | Links | BibTeX | Tags: Autonomous robotics, dynamic neural field, dynamical systems, man machine interaction, scene representation, speech recognition
@inproceedings{Zibner2010ab,
title = {Scene Representation for Anthropomorphic Robots: A Dynamic Neural Field Approach},
author = {Stephan S K U Zibner and Christian Faubel and Ioannis Iossifidis and Gregor Schöner},
url = {http://www.vde-verlag.de/proceedings-en/453273138.html},
year = {2010},
date = {2010-01-01},
booktitle = {ISR / ROBOTIK 2010},
number = {Isr},
publisher = {VDE VERLAG GmbH},
address = {Munich, Germany},
abstract = {An internal representation of a scene is essential to generate actions on scene objects. A stabilized storage of object location and features offers the flexibility to process queries phrased in human-based terms relating to objects, which may not be in the current camera view. Scene representation is therefore an internal representation of the surrounding world that is stabilized against head and body movement. It contains associated information about location and features of objects. Because objects and bodies move, scene representation is not a one-time process, but a constantly scene- adapting mechanism of scanning for, storing, updating, and deleting information.
Our novel architecture incorporates the generation of autonomous scanning sequences on real-time camera images. The head can then be oriented towards a selected object and the color feature can be extracted. Object location and feature information are associatively stored in a three-dimensional Dynamic Neural Field. Changes in the scene, even for multiple objects, can be tracked simultaneously. The stored information is used to generate behavior for cued recall. Cues can be table regions, features, or object labels. The robot demonstrates a successful recall by centering its gaze on the stated object.},
keywords = {Autonomous robotics, dynamic neural field, dynamical systems, man machine interaction, scene representation, speech recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
An internal representation of a scene is essential to generate actions on scene objects. A stabilized storage of object location and features offers the flexibility to process queries phrased in human-based terms relating to objects, which may not be in the current camera view. Scene representation is therefore an internal representation of the surrounding world that is stabilized against head and body movement. It contains associated information about location and features of objects. Because objects and bodies move, scene representation is not a one-time process, but a constantly scene- adapting mechanism of scanning for, storing, updating, and deleting information.
Our novel architecture incorporates the generation of autonomous scanning sequences on real-time camera images. The head can then be oriented towards a selected object and the color feature can be extracted. Object location and feature information are associatively stored in a three-dimensional Dynamic Neural Field. Changes in the scene, even for multiple objects, can be tracked simultaneously. The stored information is used to generate behavior for cued recall. Cues can be table regions, features, or object labels. The robot demonstrates a successful recall by centering its gaze on the stated object.
Our novel architecture incorporates the generation of autonomous scanning sequences on real-time camera images. The head can then be oriented towards a selected object and the color feature can be extracted. Object location and feature information are associatively stored in a three-dimensional Dynamic Neural Field. Changes in the scene, even for multiple objects, can be tracked simultaneously. The stored information is used to generate behavior for cued recall. Cues can be table regions, features, or object labels. The robot demonstrates a successful recall by centering its gaze on the stated object.
Stephan K U Zibner; Christian Faubel; John P Spencer; Ioannis Iossifidis; Gregor Schöner
Scenes and Tracking with Dynamic Neural Fields: How to Update a Robotic Scene Representation Proceedings Article
In: Proc. Int. Conf. on Development and Learning (ICDL10), 2010.
BibTeX | Tags: Autonomous robotics, dynamic neural field, dynamical systems, man machine interaction, scene representation, speech recognition
@inproceedings{Zibner2010c,
title = {Scenes and Tracking with Dynamic Neural Fields: How to Update a Robotic Scene Representation},
author = {Stephan K U Zibner and Christian Faubel and John P Spencer and Ioannis Iossifidis and Gregor Schöner},
year = {2010},
date = {2010-01-01},
booktitle = {Proc. Int. Conf. on Development and Learning (ICDL10)},
keywords = {Autonomous robotics, dynamic neural field, dynamical systems, man machine interaction, scene representation, speech recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
Stephan K U Zibner; Christian Faubel; Ioannis Iossifidis; Gregor Schöner
Scene Representation with Dynamic Neural Fields: An Example of Complex Cognitive Architectures Based on Dynamic Neural Field Theory Proceedings Article
In: Proc. Int. Conf. on Development and Learning (ICDL10), 2010.
BibTeX | Tags: Autonomous robotics, dynamic neural field, dynamical systems, man machine interaction, scene representation, speech recognition
@inproceedings{Zibnersubmittedb,
title = {Scene Representation with Dynamic Neural Fields: An Example of Complex Cognitive Architectures Based on Dynamic Neural Field Theory},
author = {Stephan K U Zibner and Christian Faubel and Ioannis Iossifidis and Gregor Schöner},
year = {2010},
date = {2010-01-01},
booktitle = {Proc. Int. Conf. on Development and Learning (ICDL10)},
keywords = {Autonomous robotics, dynamic neural field, dynamical systems, man machine interaction, scene representation, speech recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
Stephan Zibner; Christian Faubel; Ioannis Iossifidis; Gregor Schöner; John P Spencer
Scene and Tracking with Dynamic Neural Field Approach Proceedings Article
In: ISR / ROBOTIK 2010, Munich, Germany, 2010.
Abstract | BibTeX | Tags: Autonomous robotics, dynamic neural field, dynamical systems, man machine interaction, scene representation, speech recognition
@inproceedings{Zibneri,
title = {Scene and Tracking with Dynamic Neural Field Approach},
author = {Stephan Zibner and Christian Faubel and Ioannis Iossifidis and Gregor Schöner and John P Spencer},
year = {2010},
date = {2010-01-01},
booktitle = {ISR / ROBOTIK 2010},
address = {Munich, Germany},
abstract = {An internal representation of a scene is essential to generate actions on scene objects. A stabilized storage of object location and features offers the flexibility to process queries phrased in human-based terms relating to objects, which may not be in the current camera view. Scene representation is therefore an internal representation of the surrounding world that is stabilized against head and body movement. It contains associated information about location and features of objects. Because objects and bodies move, scene representation is not a one-time process, but a constantly scene- adapting mechanism of scanning for, storing, updating, and deleting information.
Our novel architecture incorporates the generation of autonomous scanning sequences on real-time camera images. The head can then be oriented towards a selected object and the color feature can be extracted. Object location and feature information are associatively stored in a three-dimensional Dynamic Neural Field. Changes in the scene, even for multiple objects, can be tracked simultaneously. The stored information is used to generate behavior for cued recall. Cues can be table regions, features, or object labels. The robot demonstrates a successful recall by centering its gaze on the stated object.},
keywords = {Autonomous robotics, dynamic neural field, dynamical systems, man machine interaction, scene representation, speech recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
An internal representation of a scene is essential to generate actions on scene objects. A stabilized storage of object location and features offers the flexibility to process queries phrased in human-based terms relating to objects, which may not be in the current camera view. Scene representation is therefore an internal representation of the surrounding world that is stabilized against head and body movement. It contains associated information about location and features of objects. Because objects and bodies move, scene representation is not a one-time process, but a constantly scene- adapting mechanism of scanning for, storing, updating, and deleting information.
Our novel architecture incorporates the generation of autonomous scanning sequences on real-time camera images. The head can then be oriented towards a selected object and the color feature can be extracted. Object location and feature information are associatively stored in a three-dimensional Dynamic Neural Field. Changes in the scene, even for multiple objects, can be tracked simultaneously. The stored information is used to generate behavior for cued recall. Cues can be table regions, features, or object labels. The robot demonstrates a successful recall by centering its gaze on the stated object.
Our novel architecture incorporates the generation of autonomous scanning sequences on real-time camera images. The head can then be oriented towards a selected object and the color feature can be extracted. Object location and feature information are associatively stored in a three-dimensional Dynamic Neural Field. Changes in the scene, even for multiple objects, can be tracked simultaneously. The stored information is used to generate behavior for cued recall. Cues can be table regions, features, or object labels. The robot demonstrates a successful recall by centering its gaze on the stated object.
2009
M Tuma; I Iossifidis; G Schöner
Temporal stabilization of discrete movement in variable environments: An attractor dynamics approach Proceedings Article
In: 2009 IEEE International Conference on Robotics and Automation, pp. 863–868, IEEE, 2009, ISBN: 978-1-4244-2788-8.
Abstract | Links | BibTeX | Tags: attractor dynamics approach, Autonomous robotics, dynamical systems, hopf oscillator
@inproceedings{Tuma2009b,
title = {Temporal stabilization of discrete movement in variable environments: An attractor dynamics approach},
author = {M Tuma and I Iossifidis and G Schöner},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5152562},
doi = {10.1109/ROBOT.2009.5152562},
isbn = {978-1-4244-2788-8},
year = {2009},
date = {2009-05-01},
booktitle = {2009 IEEE International Conference on Robotics and Automation},
pages = {863–868},
publisher = {IEEE},
abstract = {The ability to generate discrete movement with distinct and stable time courses is important for interaction scenarios both between different robots and with human partners, for catching and interception tasks, and for timed action sequences. In dynamic environments, where trajectories are evolving online, this is not a trivial task. The dynamical systems approach to robotics provides a framework for robust incorporation of fluctuating sensor information, but control of movement time is usually restricted to rhythmic motion and realized through stable limit cycles. The present work uses a Hopf oscillator to produce discrete motion and formulates an online adaptation rule to stabilize total movement time against a wide range of disturbances. This is integrated into a dynamical systems framework for the sequencing of movement phases and for directional navigation, using 2D-planar motion as an example. The approach is demonstrated on a Khepera mobile unit in order to show its reliability even when depending on low-level sensor information.},
keywords = {attractor dynamics approach, Autonomous robotics, dynamical systems, hopf oscillator},
pubstate = {published},
tppubtype = {inproceedings}
}
The ability to generate discrete movement with distinct and stable time courses is important for interaction scenarios both between different robots and with human partners, for catching and interception tasks, and for timed action sequences. In dynamic environments, where trajectories are evolving online, this is not a trivial task. The dynamical systems approach to robotics provides a framework for robust incorporation of fluctuating sensor information, but control of movement time is usually restricted to rhythmic motion and realized through stable limit cycles. The present work uses a Hopf oscillator to produce discrete motion and formulates an online adaptation rule to stabilize total movement time against a wide range of disturbances. This is integrated into a dynamical systems framework for the sequencing of movement phases and for directional navigation, using 2D-planar motion as an example. The approach is demonstrated on a Khepera mobile unit in order to show its reliability even when depending on low-level sensor information.
Matthias Tuma; Ioannis Iossifidis; Gregor Schöner
Temporal Stabilization of Discrete Movement in Variable Environments: An Attractor Dynamics Approach Proceedings Article
In: Proc. IEEE International Conference on Robotics and Automation ICRA '09, pp. 863–868, Kobe, Japan, 2009.
Abstract | BibTeX | Tags: attractor dynamics approach, Autonomous robotics, dynamical systems, hopf oscillator
@inproceedings{Tuma2009,
title = {Temporal Stabilization of Discrete Movement in Variable Environments: An Attractor Dynamics Approach},
author = {Matthias Tuma and Ioannis Iossifidis and Gregor Schöner},
year = {2009},
date = {2009-01-01},
booktitle = {Proc. IEEE International Conference on Robotics and Automation ICRA '09},
pages = {863–868},
address = {Kobe, Japan},
abstract = {The ability to generate discrete movement with distinct and stable time courses
is important for interaction scenarios both between different robots and with human partners,
for catching and interception tasks, and for timed action sequences.
In dynamic environments, where trajectories are evolving on-line, this is not a trivial task.
The dynamical systems approach to robotics provides a framework for robust
incorporation of fluctuating sensor information, but control of movement time is usually
restricted to rhythmic motion and realized through stable limit cycles. The present work
uses a Hopf oscillator to produce discrete motion and formulates an on-line adaptation rule
to stabilize total movement time against a wide range of disturbances. This is integrated into
a dynamical systems framework for the sequencing of movement phases and for directional navigation, using 2D-planar motion
as an example. The approach is demonstrated on a Khepera mobile unit in order to show its
reliability even when depending on low-level sensor information.},
keywords = {attractor dynamics approach, Autonomous robotics, dynamical systems, hopf oscillator},
pubstate = {published},
tppubtype = {inproceedings}
}
The ability to generate discrete movement with distinct and stable time courses
is important for interaction scenarios both between different robots and with human partners,
for catching and interception tasks, and for timed action sequences.
In dynamic environments, where trajectories are evolving on-line, this is not a trivial task.
The dynamical systems approach to robotics provides a framework for robust
incorporation of fluctuating sensor information, but control of movement time is usually
restricted to rhythmic motion and realized through stable limit cycles. The present work
uses a Hopf oscillator to produce discrete motion and formulates an on-line adaptation rule
to stabilize total movement time against a wide range of disturbances. This is integrated into
a dynamical systems framework for the sequencing of movement phases and for directional navigation, using 2D-planar motion
as an example. The approach is demonstrated on a Khepera mobile unit in order to show its
reliability even when depending on low-level sensor information.
is important for interaction scenarios both between different robots and with human partners,
for catching and interception tasks, and for timed action sequences.
In dynamic environments, where trajectories are evolving on-line, this is not a trivial task.
The dynamical systems approach to robotics provides a framework for robust
incorporation of fluctuating sensor information, but control of movement time is usually
restricted to rhythmic motion and realized through stable limit cycles. The present work
uses a Hopf oscillator to produce discrete motion and formulates an on-line adaptation rule
to stabilize total movement time against a wide range of disturbances. This is integrated into
a dynamical systems framework for the sequencing of movement phases and for directional navigation, using 2D-planar motion
as an example. The approach is demonstrated on a Khepera mobile unit in order to show its
reliability even when depending on low-level sensor information.
Ioannis Iossifidis; Gregor Schöner
Reaching while avoiding obstacles: a neuronally inspired attractor dynamics approach Proceedings Article
In: Bernstein Conference on Computational Neuroscience (BCCN 2009), 2009.
Links | BibTeX | Tags: central nervous system, collision avoidance, dynamical systems, manipulator dynamics, obstacle avoidance, redundant robot arm
@inproceedings{Iossifidis2009,
title = {Reaching while avoiding obstacles: a neuronally inspired attractor dynamics approach},
author = {Ioannis Iossifidis and Gregor Schöner},
doi = {10.3389/conf.neuro.10.2009.14.007},
year = {2009},
date = {2009-01-01},
booktitle = {Bernstein Conference on Computational Neuroscience (BCCN 2009)},
keywords = {central nervous system, collision avoidance, dynamical systems, manipulator dynamics, obstacle avoidance, redundant robot arm},
pubstate = {published},
tppubtype = {inproceedings}
}
2006
Ioannis Iossifidis
Dynamische Systeme zur Steuerung anthropomorpher Roboterarme in autonomen Robotersystemen Book
Logos Verlag Berlin, 2006.
Abstract | Links | BibTeX | Tags: Autonomous robotics, dynamical systems, Inverse kinematics
@book{Iossifidis2006b,
title = {Dynamische Systeme zur Steuerung anthropomorpher Roboterarme in autonomen Robotersystemen},
author = {Ioannis Iossifidis},
url = {http://www.logos-verlag.de/cgi-bin/engbuchmid?isbn=1305&lng=deu&id=},
year = {2006},
date = {2006-08-01},
urldate = {2006-08-01},
number = {ISBN: 3-8325-1305-1},
pages = {160},
publisher = {Logos Verlag Berlin},
abstract = {Das übergeordnete Forschungsgebiet, in das sich die vorliegende Arbeit einbettet, befasst sich mit der Erforschung von informationsverabeitenden Prozessen im Gehirn und der Anwendung der resultierenden Erkenntnisse auf technische Systeme.
In Analogie zu biologischen Systemen, deren Beschaffenheit aus den Anforderungen der Umwelt an ihr Verhalten resultiert, leitet sich die Anthropomorphie als Entwurfsprinzip für die Struktur des mit den Menschen interagierenden robotischen Assistenzsystemen ab.
Der Autor behandelt in der vorliegende Arbeit das Problem der Erzeugung von Motorverhalten im dreidimensionalen Raum am Beispiel eines anthropomorphen Roboterarmes in einem anthropomorphen robotischen Assistenzsystem.
Entwickelt wurde hierbei ein allgemeiner Ansatz, der die Konzepte der Erzeugung von Motorverhalten im 3D-Raum, der Voraussimulation dynamischer Systeme zur Systemdiagnose und zur Suche gewünschter Systemzustände, sowie ein Konzept der Organisation von Verhalten enthält und vereinigt.
Nichtlineare dynamische Systeme bilden das mathematische Fundament, die einheitlich, formale Sprache des Ansatzes, mit der sowohl das Motorverhalten des Roboters als auch dessen zeitkontinuierliche Teilsysteme rückgekoppelt werden.},
keywords = {Autonomous robotics, dynamical systems, Inverse kinematics},
pubstate = {published},
tppubtype = {book}
}
Das übergeordnete Forschungsgebiet, in das sich die vorliegende Arbeit einbettet, befasst sich mit der Erforschung von informationsverabeitenden Prozessen im Gehirn und der Anwendung der resultierenden Erkenntnisse auf technische Systeme.
In Analogie zu biologischen Systemen, deren Beschaffenheit aus den Anforderungen der Umwelt an ihr Verhalten resultiert, leitet sich die Anthropomorphie als Entwurfsprinzip für die Struktur des mit den Menschen interagierenden robotischen Assistenzsystemen ab.
Der Autor behandelt in der vorliegende Arbeit das Problem der Erzeugung von Motorverhalten im dreidimensionalen Raum am Beispiel eines anthropomorphen Roboterarmes in einem anthropomorphen robotischen Assistenzsystem.
Entwickelt wurde hierbei ein allgemeiner Ansatz, der die Konzepte der Erzeugung von Motorverhalten im 3D-Raum, der Voraussimulation dynamischer Systeme zur Systemdiagnose und zur Suche gewünschter Systemzustände, sowie ein Konzept der Organisation von Verhalten enthält und vereinigt.
Nichtlineare dynamische Systeme bilden das mathematische Fundament, die einheitlich, formale Sprache des Ansatzes, mit der sowohl das Motorverhalten des Roboters als auch dessen zeitkontinuierliche Teilsysteme rückgekoppelt werden.
In Analogie zu biologischen Systemen, deren Beschaffenheit aus den Anforderungen der Umwelt an ihr Verhalten resultiert, leitet sich die Anthropomorphie als Entwurfsprinzip für die Struktur des mit den Menschen interagierenden robotischen Assistenzsystemen ab.
Der Autor behandelt in der vorliegende Arbeit das Problem der Erzeugung von Motorverhalten im dreidimensionalen Raum am Beispiel eines anthropomorphen Roboterarmes in einem anthropomorphen robotischen Assistenzsystem.
Entwickelt wurde hierbei ein allgemeiner Ansatz, der die Konzepte der Erzeugung von Motorverhalten im 3D-Raum, der Voraussimulation dynamischer Systeme zur Systemdiagnose und zur Suche gewünschter Systemzustände, sowie ein Konzept der Organisation von Verhalten enthält und vereinigt.
Nichtlineare dynamische Systeme bilden das mathematische Fundament, die einheitlich, formale Sprache des Ansatzes, mit der sowohl das Motorverhalten des Roboters als auch dessen zeitkontinuierliche Teilsysteme rückgekoppelt werden.
I Iossifidis; G Schöner
Reaching with a redundant anthropomorphic robot arm using attractor dynamics Book Section
In: VDI Berichte, no. 1956, 2006, ISSN: 00835560.
Abstract | BibTeX | Tags: dynamical systems, Kinematics, Motion planning, Redundant manipulator
@incollection{Iossifidis2006d,
title = {Reaching with a redundant anthropomorphic robot arm using attractor dynamics},
author = {I Iossifidis and G Schöner},
issn = {00835560},
year = {2006},
date = {2006-01-01},
booktitle = {VDI Berichte},
number = {1956},
abstract = {The attractor dynamics approach has been extended to generate goal directed movement of a redundant, anthropomorphic arm while avoiding dynamic obstacles and respecting joint limits. A straight-line trajectory has been generated to make the robot's movement human like, using two heading direction angles of the tool-point which is quite analogous to the movement represented in the primate central nervous system. Two additional angles control the tool's spatial orientation so that it follows the tool-point's collision-free path. The attractor dynamics approach to trajectory formation is generalized to the control of the motion of a redundant robot arm by applying a task decomposition, comprising endeffector, wrist and elbow motion. This new approach, implemented on the autonomous robotic assistant Cora, enables collision free movement in a considerably wide set of obstacles and target geometries.},
keywords = {dynamical systems, Kinematics, Motion planning, Redundant manipulator},
pubstate = {published},
tppubtype = {incollection}
}
The attractor dynamics approach has been extended to generate goal directed movement of a redundant, anthropomorphic arm while avoiding dynamic obstacles and respecting joint limits. A straight-line trajectory has been generated to make the robot's movement human like, using two heading direction angles of the tool-point which is quite analogous to the movement represented in the primate central nervous system. Two additional angles control the tool's spatial orientation so that it follows the tool-point's collision-free path. The attractor dynamics approach to trajectory formation is generalized to the control of the motion of a redundant robot arm by applying a task decomposition, comprising endeffector, wrist and elbow motion. This new approach, implemented on the autonomous robotic assistant Cora, enables collision free movement in a considerably wide set of obstacles and target geometries.
Ioannis Iossifidis
Dynamische Systeme zur Steuerung anthropomorpher Roboterarme in autonomen Robotersystemen PhD Thesis
Faculty for Physics and Astronomy, Ruhr-University Bochum, 2006.
Abstract | Links | BibTeX | Tags: Autonomous robotics, dynamical systems, Inverse kinematics
@phdthesis{Iossifidis2006c,
title = {Dynamische Systeme zur Steuerung anthropomorpher Roboterarme in autonomen Robotersystemen},
author = {Ioannis Iossifidis},
url = {http://www.logos-verlag.de/cgi-bin/engbuchmid?isbn=1305&lng=deu&id=},
year = {2006},
date = {2006-01-01},
urldate = {2006-01-01},
number = {ISBN: 3-8325-1305-1},
pages = {160},
publisher = {Logos Verlag Berlin},
address = {Bochum, Germany},
school = {Faculty for Physics and Astronomy, Ruhr-University Bochum},
abstract = {Das übergeordnete Forschungsgebiet, in das sich die vorliegende Arbeit einbettet, befasst sich mit der Erforschung von informationsverabeitenden Prozessen im Gehirn und der Anwendung der resultierenden Erkenntnisse auf technische Systeme. In Analogie zu biologischen Systemen, deren Beschaffenheit aus den Anforderungen der Umwelt an ihr Verhalten resultiert, leitet sich die Anthropomorphie als Entwurfsprinzip für die Struktur des mit den Menschen interagierenden robotischen Assistenzsystemen ab. Der Autor behandelt in der vorliegende Arbeit das Problem der Erzeugung von Motorverhalten im dreidimensionalen Raum am Beispiel eines anthropomorphen Roboterarmes in einem anthropomorphen robotischen Assistenzsystem. Entwickelt wurde hierbei ein allgemeiner Ansatz, der die Konzepte der Erzeugung von Motorverhalten im 3D-Raum, der Voraussimulation dynamischer Systeme zur Systemdiagnose und zur Suche gewünschter Systemzustände, sowie ein Konzept der Organisation von Verhalten enthält und vereinigt. Nichtlineare dynamische Systeme bilden das mathematische Fundament, die einheitlich, formale Sprache des Ansatzes, mit der sowohl das Motorverhalten des Roboters als auch dessen zeitkontinuierliche Teilsysteme rückgekoppelt werden.},
keywords = {Autonomous robotics, dynamical systems, Inverse kinematics},
pubstate = {published},
tppubtype = {phdthesis}
}
Das übergeordnete Forschungsgebiet, in das sich die vorliegende Arbeit einbettet, befasst sich mit der Erforschung von informationsverabeitenden Prozessen im Gehirn und der Anwendung der resultierenden Erkenntnisse auf technische Systeme. In Analogie zu biologischen Systemen, deren Beschaffenheit aus den Anforderungen der Umwelt an ihr Verhalten resultiert, leitet sich die Anthropomorphie als Entwurfsprinzip für die Struktur des mit den Menschen interagierenden robotischen Assistenzsystemen ab. Der Autor behandelt in der vorliegende Arbeit das Problem der Erzeugung von Motorverhalten im dreidimensionalen Raum am Beispiel eines anthropomorphen Roboterarmes in einem anthropomorphen robotischen Assistenzsystem. Entwickelt wurde hierbei ein allgemeiner Ansatz, der die Konzepte der Erzeugung von Motorverhalten im 3D-Raum, der Voraussimulation dynamischer Systeme zur Systemdiagnose und zur Suche gewünschter Systemzustände, sowie ein Konzept der Organisation von Verhalten enthält und vereinigt. Nichtlineare dynamische Systeme bilden das mathematische Fundament, die einheitlich, formale Sprache des Ansatzes, mit der sowohl das Motorverhalten des Roboters als auch dessen zeitkontinuierliche Teilsysteme rückgekoppelt werden.
2005
I Iossifidis; A Steinhage
Behavior generation for Anthropomorphic robots by means of dynamical systems Book
2005, ISSN: 16107438.
Abstract | BibTeX | Tags: Autonomous robotics, behavior generation, dynamical systems, movement model
@book{Iossifidis2005c,
title = {Behavior generation for Anthropomorphic robots by means of dynamical systems},
author = {I Iossifidis and A Steinhage},
issn = {16107438},
year = {2005},
date = {2005-01-01},
booktitle = {Springer Tracts in Advanced Robotics},
volume = {14},
abstract = {This article describes the current state of our research on anthropomorphic robots. Our aim is to make the reader familiar with the two basic principles our work is based on: anthropomorphism and dynamics. The principle of anthropomorphism means a restriction to human-like robots which use version, audition and touch as their only sensors so that natural man-machine interaction is possible. The principle of dynamics stands for the mathematical framework based on which our robots generate their behavior. Both principles have their root in the idea that concepts of biological behavior and information processing can be exploited to control technical systems. textcopyright Springer-Verlag Berlin Heidelberg 2005.},
keywords = {Autonomous robotics, behavior generation, dynamical systems, movement model},
pubstate = {published},
tppubtype = {book}
}
This article describes the current state of our research on anthropomorphic robots. Our aim is to make the reader familiar with the two basic principles our work is based on: anthropomorphism and dynamics. The principle of anthropomorphism means a restriction to human-like robots which use version, audition and touch as their only sensors so that natural man-machine interaction is possible. The principle of dynamics stands for the mathematical framework based on which our robots generate their behavior. Both principles have their root in the idea that concepts of biological behavior and information processing can be exploited to control technical systems. textcopyright Springer-Verlag Berlin Heidelberg 2005.
2004
Erwin Prassler; Gisbert Lawitzky; Andreas Stopp; Gerhard Grunwald; Martin Ħägele; Rüdiger Đillmann; Ioannis Iossifidis
Advances in Ħuman Robot Interaction Book
Springer Press, 2004.
Abstract | Links | BibTeX | Tags: Autonomous robotics, behavior generation, dynamical systems, movement model
@book{Prassler2004,
title = {Advances in Ħuman Robot Interaction},
author = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Ħägele and Rüdiger Đillmann and Ioannis Iossifidis},
editor = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Ħägele and Rüdiger Đillmann and Ioannis Iossifidis},
url = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-102-22-35029562-0,00.html?changeHeader=true},
year = {2004},
date = {2004-01-01},
booktitle = {Advances in Ħuman Robot Interaction},
volume = {14/2004},
number = {ISBN: 3-540-23211-7},
pages = {414},
publisher = {Springer Press},
series = {Springer Tracts in Advanced Robotics STAR},
abstract = {Human Robot Interaction and Cooperation
Motion Coordination
Multi-Modal Robot Interfaces
Physical Interaction between Humans and Robots
Robot Learning
Visual Instruction of Robots},
keywords = {Autonomous robotics, behavior generation, dynamical systems, movement model},
pubstate = {published},
tppubtype = {book}
}
Human Robot Interaction and Cooperation
Motion Coordination
Multi-Modal Robot Interfaces
Physical Interaction between Humans and Robots
Robot Learning
Visual Instruction of Robots
Motion Coordination
Multi-Modal Robot Interfaces
Physical Interaction between Humans and Robots
Robot Learning
Visual Instruction of Robots
Ioannis Iossifidis; Gregor Schöner
Attractor dynamics approach for autonomous collision-free path generation in 3d-space for an 7 dof robot arm Proceedings Article
In: Proceedings of the ROBOTIK 2004, Leistungsstand - Anwendungen - Visionen - Trends, number 1841 in VDI-Berichte, pp. 815–822, VDI/VDE VDI Verlag, München, Germany, 2004.
BibTeX | Tags: Autonomous robotics, collision avoidance, dynamical systems, Inverse kinematics, movement model
@inproceedings{Iossifidis2004a,
title = {Attractor dynamics approach for autonomous collision-free path generation in 3d-space for an 7 dof robot arm},
author = {Ioannis Iossifidis and Gregor Schöner},
year = {2004},
date = {2004-01-01},
booktitle = {Proceedings of the ROBOTIK 2004, Leistungsstand - Anwendungen - Visionen - Trends, number 1841 in VDI-Berichte},
pages = {815–822},
publisher = {VDI Verlag},
address = {München, Germany},
organization = {VDI/VDE},
keywords = {Autonomous robotics, collision avoidance, dynamical systems, Inverse kinematics, movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
Ioannis Iossifidis; Carsten Bruckhoff; Christoph Theis; Claudia Grote; Christian Faubel; Gregor Schöner
A Cooperative Robot Assistant CoRA For Human Environments Book Section
In: Prassler, Erwin; Lawitzky, Gisbert; Stopp, Andreas; Grunwald, Gerhard; Hägele, Martin; Dillmann, Rüdiger; Iossifidis, Ioannis (Ed.): Advances in Human Robot Interaction, vol. 14/2004, no. ISBN: 3-540-23211-7,, pp. 385–401, Springer Press, 2004, ISBN: 3-540-23211-7,.
Abstract | Links | BibTeX | Tags: Autonomous robotics, behavior generation, dynamical systems, movement model
@incollection{Iossifidis2004d,
title = {A Cooperative Robot Assistant CoRA For Human Environments},
author = {Ioannis Iossifidis and Carsten Bruckhoff and Christoph Theis and Claudia Grote and Christian Faubel and Gregor Schöner},
editor = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Hägele and Rüdiger Dillmann and Ioannis Iossifidis},
url = {http://www.springerlink.com/index/91656F7B99CD2C2C},
doi = {10.1007/b97960},
isbn = {3-540-23211-7,},
year = {2004},
date = {2004-01-01},
booktitle = {Advances in Human Robot Interaction},
volume = {14/2004},
number = {ISBN: 3-540-23211-7,},
pages = {385–401},
publisher = {Springer Press},
chapter = {7},
series = {Springer Tracts in Advanced Robotics STAR},
abstract = {CoRA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand-over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robotrsquos gripper (force sensing). The design objective has been to exploit the human operatorrsquos intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.},
keywords = {Autonomous robotics, behavior generation, dynamical systems, movement model},
pubstate = {published},
tppubtype = {incollection}
}
CoRA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand-over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robotrsquos gripper (force sensing). The design objective has been to exploit the human operatorrsquos intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.
Ioannis Iossifidis; Axel Steinhage
Behavior Generation For Anthropomorphic Robots by Means of Dynamical Systems Book Section
In: Prassler, Erwin; Lawitzky, Gisbert; Stopp, Andreas; Grunwald, Gerhard; Hägele, Martin; Dillmann, Rüdiger; Iossifidis, Ioannis (Ed.): Advances in Human Robot Interaction, vol. 14/2004, no. ISBN: 3-540-23211-7,, pp. 269–300, Springer Press, 2004, ISBN: 3-540-23211-7,.
Abstract | Links | BibTeX | Tags: Autonomous robotics, behavior generation, dynamical systems, movement model
@incollection{Iossifidis2004e,
title = {Behavior Generation For Anthropomorphic Robots by Means of Dynamical Systems},
author = {Ioannis Iossifidis and Axel Steinhage},
editor = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Hägele and Rüdiger Dillmann and Ioannis Iossifidis},
url = {http://www.springerlink.com/index/96DD6AB012CF71E7},
doi = {0.1007/b97960},
isbn = {3-540-23211-7,},
year = {2004},
date = {2004-01-01},
booktitle = {Advances in Human Robot Interaction},
volume = {14/2004},
number = {ISBN: 3-540-23211-7,},
pages = {269–300},
publisher = {Springer Press},
chapter = {6},
series = {Springer Tracts in Advanced Robotics STAR},
abstract = {This article describes the current state of our research on anthropomorphic robots. Our aim is to make the reader familiar with the two basic principles our work is based on: anthropomorphism and dynamics. The principle of anthropomorphism means a restriction to human-like robots which use version, audition and touch as their only sensors so that natural man-machine interaction is possible. The principle of dynamics stands for the mathematical framework based on which our robots generate their behavior. Both principles have their root in the idea that concepts of biological behavior and information processing can be exploited to control technical systems.},
keywords = {Autonomous robotics, behavior generation, dynamical systems, movement model},
pubstate = {published},
tppubtype = {incollection}
}
This article describes the current state of our research on anthropomorphic robots. Our aim is to make the reader familiar with the two basic principles our work is based on: anthropomorphism and dynamics. The principle of anthropomorphism means a restriction to human-like robots which use version, audition and touch as their only sensors so that natural man-machine interaction is possible. The principle of dynamics stands for the mathematical framework based on which our robots generate their behavior. Both principles have their root in the idea that concepts of biological behavior and information processing can be exploited to control technical systems.
Erwin Prassler; Gisbert Lawitzky; Andreas Stopp; Gerhard Grunwald; Martin Hägele; Rüdiger Dillmann; Ioannis Iossifidis
Advances in Human Robot Interaction Book
Springer Press, 2004.
Abstract | Links | BibTeX | Tags: Autonomous robotics, behavior generation, dynamical systems, Inverse kinematics, movement model, redundant robot arm
@book{Prassler2004b,
title = {Advances in Human Robot Interaction},
author = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Hägele and Rüdiger Dillmann and Ioannis Iossifidis},
editor = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Hägele and Rüdiger Dillmann and Ioannis Iossifidis},
url = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-102-22-35029562-0,00.html?changeHeader=true},
year = {2004},
date = {2004-01-01},
booktitle = {Advances in Human Robot Interaction},
volume = {14/2004},
pages = {414},
publisher = {Springer Press},
series = {Springer Tracts in Advanced Robotics STAR},
abstract = {Human Robot Interaction and Cooperation Motion Coordination Multi-Modal Robot Interfaces Physical Interaction between Humans and Robots Robot Learning Visual Instruction of Robots},
keywords = {Autonomous robotics, behavior generation, dynamical systems, Inverse kinematics, movement model, redundant robot arm},
pubstate = {published},
tppubtype = {book}
}
Human Robot Interaction and Cooperation Motion Coordination Multi-Modal Robot Interfaces Physical Interaction between Humans and Robots Robot Learning Visual Instruction of Robots
Erwin Prassler; Gisbert Lawitzky; Andreas Stopp; Gerhard Grunwald; Martin Hägele; Rüdiger Dillmann; Ioannis Iossifidis
Advances in Human Robot Interaction (Springer Tracts in Advanced Robotics) Book
Springer, 2004, ISBN: 3540232117.
Links | BibTeX | Tags: Autonomous robotics, behavior generation, dynamical systems, Inverse kinematics, movement model, redundant robot arm
@book{Prassler2004c,
title = {Advances in Human Robot Interaction (Springer Tracts in Advanced Robotics)},
author = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Hägele and Rüdiger Dillmann and Ioannis Iossifidis},
url = {http://www.amazon.co.uk/Advances-Interaction-Springer-Advanced-Robotics/dp/3540232117},
isbn = {3540232117},
year = {2004},
date = {2004-01-01},
pages = {414},
publisher = {Springer},
keywords = {Autonomous robotics, behavior generation, dynamical systems, Inverse kinematics, movement model, redundant robot arm},
pubstate = {published},
tppubtype = {book}
}
2001
I Iossifidis; A Steinhage
Dynamical systems: A framework for man machine interaction Book
2001, ISBN: 9608052440.
Abstract | BibTeX | Tags: Anthropomorphic Robot, Dynamic Approach, dynamical systems
@book{Iossifidis2001a,
title = {Dynamical systems: A framework for man machine interaction},
author = {I Iossifidis and A Steinhage},
isbn = {9608052440},
year = {2001},
date = {2001-01-01},
booktitle = {Advances in Automation, Multimedia and Video Systems, and Modern Computer Science},
abstract = {We present an architecture to generate behavior for anthropomorphic robots. The goal is to equip the robots with the capacity to interact naturally with a human sharing the same interaction-channels. Motivated by the research on biological systems, our basic assumption is that the behavior to perform determines the external and internal structure of the behaving system. We describe the anthropomorphic design of our robots and present a distributed control system that generates human-like navigation and manipulation behavior. As the mathematical framework for this purpose we have developed a control system which is entirely based on dynamical systems in the form of instantiated dynamics and neural fields.},
keywords = {Anthropomorphic Robot, Dynamic Approach, dynamical systems},
pubstate = {published},
tppubtype = {book}
}
We present an architecture to generate behavior for anthropomorphic robots. The goal is to equip the robots with the capacity to interact naturally with a human sharing the same interaction-channels. Motivated by the research on biological systems, our basic assumption is that the behavior to perform determines the external and internal structure of the behaving system. We describe the anthropomorphic design of our robots and present a distributed control system that generates human-like navigation and manipulation behavior. As the mathematical framework for this purpose we have developed a control system which is entirely based on dynamical systems in the form of instantiated dynamics and neural fields.
Ioannis Iossifidis; Axel Steinhage
Dynamical Systems: A Framework for Man Machine Interaction Proceedings Article
In: Kluev, V V; D'Attellis, C E; Mastorakis, N E (Ed.): Proceedings of the International Conference On Automation and Information: Theory and Applications 2001, (AITA 2001), WSES WSES Press, Skiathos Island, Greece, 2001.
Abstract | BibTeX | Tags: Anthropomorphic Robot, Dynamic Approach, dynamical systems
@inproceedings{Iossifidis2001b,
title = {Dynamical Systems: A Framework for Man Machine Interaction},
author = {Ioannis Iossifidis and Axel Steinhage},
editor = {V V Kluev and C E D'Attellis and N E Mastorakis},
year = {2001},
date = {2001-01-01},
booktitle = {Proceedings of the International Conference On Automation and Information: Theory and Applications 2001, (AITA 2001)},
number = {ISBN:960-8052-44-0},
publisher = {WSES Press},
address = {Skiathos Island, Greece},
organization = {WSES},
abstract = {We present an architecture to generate behavior for anthropomorphic robots. The goal is to equip the robots with the capacity to interact naturally with a human sharing the same interaction- channels. Motivated by the research on biological systems, our basic assumption is that the behavior to perform determines the external and internal structure of the behaving system. We describe the anthropomorphic design of our robots and present a distributed control system that generates human- like navigation and manipulation behavior. As the mathematical framework for this purpose we have developed a control system which is entirely based on dynamical systems in the form of instantiated dynamics and neural fields.},
keywords = {Anthropomorphic Robot, Dynamic Approach, dynamical systems},
pubstate = {published},
tppubtype = {inproceedings}
}
We present an architecture to generate behavior for anthropomorphic robots. The goal is to equip the robots with the capacity to interact naturally with a human sharing the same interaction- channels. Motivated by the research on biological systems, our basic assumption is that the behavior to perform determines the external and internal structure of the behaving system. We describe the anthropomorphic design of our robots and present a distributed control system that generates human- like navigation and manipulation behavior. As the mathematical framework for this purpose we have developed a control system which is entirely based on dynamical systems in the form of instantiated dynamics and neural fields.