Publications

@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 = {dynamical systems, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@inproceedings{bccn18b,

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 = {dynamical systems, movement model, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@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}

}

@inproceedings{Rano2013b,

title = {Modelling human arm motion through the attractor dynamics approach},

author = {Inaki Rano and Ioannis Iossifidis},

doi = {10.1109/ROBIO.2013.6739777},

isbn = {9781479927449},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013},

pages = {2088--2093},

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

keywords = {arm movement model, dynamical systems, movement model, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2013ac,

title = {Modeling Human Arm Motion by Means of Attractor Dynamics Approach},

author = {Ioannis Iossifidis and Ianki Rano},

year  = {2013},

date = {2013-01-01},

booktitle = {Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2013)},

abstract = {Autonomous robots with limited computational capacity call for control approaches that generate meaningful, goal-directed behavior without using a large amount of resources. The attractor dynamics approach to movement generation is a framework that links sensor data to motor commands via coupled dynamical systems that have attractors at behaviorally desired states. The low computational demands leave enough system resources for higher level function like forming a sequence of local goals to reach a distant one. The comparatively high performance of local behavior generation allows the global planning to be relatively simple. 

 

 In the present paper, we apply this approach to generate walking trajectories for a small humanoid robot, the Aldebaran Nao, that are goal-directed and avoid obstacles. The sensor information is a single camera in the head of the robot. The limited field of vision is compensated by head movements. The design of the dynamical system for motion generation and the choice of state variable makes a computationally expensive scene representation or local map building unnecessary.},

keywords = {arm movement model, dynamical systems, movement model, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@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}

}

@inproceedings{Reimann2011b,

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}

}

@inproceedings{Iossifidis2011Ab,

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}

}

@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, dynamical systems, Inverse kinematics, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Reimann2010ab,

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, dynamical systems, Inverse kinematics, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@inproceedings{Reimann2010bb,

title = {Generating collision free reaching movements for redundant manipulators using dynamical systems},

author = {H Reimann and Ioannis 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},

urldate = {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 = {anthropomorphic arm, attractor dynamics approach, autonomous obstacle avoidance, central nervous system, collision avoidance, dynamical systems, manipulator dynamics, redundant manipulators, redundant robot arm},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {dynamic field theory (DFT), dynamical systems, embodied cognition, neural processing, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {dynamic field theory (DFT), dynamical systems, embodied cognition, neural processing, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {behavior generation, dynamical systems, movement model, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {behavior generation, dynamical systems, movement model, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {behavior generation, dynamical systems, movement model, movile robot, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {behavior generation, dynamical systems, man machine interaction, movement model, Robotics, speech recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {dynamic neural field, dynamical systems, man machine interaction, Robotics, scene representation, speech recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {dynamic neural field, dynamical systems, man machine interaction, Robotics, scene representation, speech recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {dynamic neural field, dynamical systems, man machine interaction, Robotics, scene representation, speech recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {dynamic neural field, dynamical systems, man machine interaction, Robotics, scene representation, speech recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zibner2010f,

title = {Scenes and tracking with dynamic neural fields: How to update a robotic scene representation},

author = {S K U Zibner and Christian Faubel and Ioannis Iossifidis and G Schöner and J P Spencer},

doi = {10.1109/DEVLRN.2010.5578837},

isbn = {9781424469024},

year  = {2010},

date = {2010-01-01},

urldate = {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 = {dynamic field theory (DFT), dynamical systems, embodied cognition, neural processing, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zibner2010bb,

title = {Scene representation for anthropomorphic robots: A dynamic neural field approach},

author = {S K U Zibner and Christian Faubel and Ioannis Iossifidis and G Schöner},

isbn = {9781617387197},

year  = {2010},

date = {2010-01-01},

urldate = {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 = {dynamic field theory (DFT), dynamical systems, embodied cognition, neural processing, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Reimanndb,

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 = {arm movement model, behavior generation, dynamical systems, movement model, obstacle avoidance, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Grimm2010bb,

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 = {behavior generation, dynamical systems, movement model, movile robot, Robotics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sandamirskayasubmittedb,

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 = {arm movement model, behavior generation, dynamical systems, man machine interaction, movement model, Robotics, speech recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zibner2010abb,

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 = {dynamic neural field, dynamical systems, man machine interaction, Robotics, scene representation, speech recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zibner2010cb,

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 = {dynamic neural field, dynamical systems, man machine interaction, Robotics, scene representation, speech recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zibnersubmittedbb,

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 = {dynamic neural field, dynamical systems, man machine interaction, Robotics, scene representation, speech recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zibnerib,

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 = {dynamic neural field, dynamical systems, man machine interaction, Robotics, scene representation, speech recognition},

pubstate = {published},

tppubtype = {inproceedings}

}