Publications

@inproceedings{Iossifidis2014a,

title = {Simulated Framework for the Development and Evaluation of Redundant Robotic Systems},

author = {Ioannis Iossifidis},

year  = {2014},

date = {2014-01-01},

booktitle = {International Conference on Pervasive and Embedded and Communication Systems, 2014, PECCS2014},

abstract = {In the current work we present a simulated environment for the development and evaluation of multi redundant open chain manipulators. The framework is implemented in Matlab and provides solutions for the kinematics and dynamics of an arbitrary open chain manipulator. For a anthropomorphic trunk-shoulder-arm configura- tion with in total nine degree of freedoms, a closed form solution of the inverse kinematics problem is derived. The attractor dynamics approach to motion generation was evaluated within this framework and the results are verified on the real anthropomorphic robotic assistant Cora.},

keywords = {Autonomous robotics, man machine interaction, simulated reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2014b,

title = {Development of a Haptic Interface for Safe Human Robot Collaboration},

author = {Ioannis Iossifidis},

year  = {2014},

date = {2014-01-01},

booktitle = {International Conference on Pervasive and Embedded and Communication Systems, 2014, PECCS2014},

abstract = {In the context of the increasing number of collaborative workplaces in industrial environments, where humans and robots sharing the same workplace, safety and intuitive interaction is a prerequisite. This means, that the robot can (1) have contact with his own body and the surrounding objects, (2) the motion of the robot can be corrected online by the human user just by touching his artificial skin or (3) interrupt the action in dangerous situations. In the current work we introduce a haptic interface (artificial skin) which is utilized to cover the arms of an anthropomorphic robotic assistant. The touched induced input of the artificial skin is interpreted and fed into the motor control algorithm to generate the desired motion and to avoid harm for human and machine.},

keywords = {Autonomous robotics, direct physical interaction, haptic interface, human robot collaboration, man machine interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2014c,

title = {Development of a Haptic Interface for Safe Human Robot Collaboration},

author = {Ioannis Iossifidis},

year  = {2014},

date = {2014-01-01},

booktitle = {International Conference on Pervasive and Embedded and Communication Systems, 2012, PECCS2014},

keywords = {Autonomous robotics, direct physical interaction, haptic interface, human robot collaboration, man machine interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2014ab,

title = {Simulated Framework for the Development and Evaluation of Redundant Robotic Systems},

author = {Ioannis Iossifidis},

year  = {2014},

date = {2014-01-01},

booktitle = {International Conference on Pervasive and Embedded and Communication Systems, 2012, PECCS2014},

keywords = {Autonomous robotics, man machine interaction, simulated reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2014e,

title = {Development of a Haptic Interface for Safe Human Robot Collaboration},

author = {Ioannis Iossifidis},

year  = {2014},

date = {2014-01-01},

booktitle = {International Conference on Pervasive and Embedded and Communication Systems, 2012, PECCS2014},

keywords = {Autonomous robotics, direct physical interaction, haptic interface, human robot collaboration, man machine interaction},

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{Iossifidis2013c,

title = {Utilizing artificial skin for direct physical interaction},

author = {I Iossifidis},

doi = {10.1109/ROBIO.2013.6739562},

year  = {2013},

date = {2013-01-01},

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

abstract = {Focusing on the development of flexible robots for industrial and household environments, we identify intuitive teaching as the key feature and direct physical interaction and guidance as the most important interface. In the current work we introduce a multi redundant robotic assistant equipped with a touch sensitive skin around the upper- and the forearm, in order to incorporate contact forces into the arm control. A context-sensitive interpretation of the contact forces is being used to guide the attention of the robot, to avoid obstacles and to move the robot arm directly by the human operator. textcopyright 2013 IEEE.},

keywords = {Autonomous robotics, direct physical interaction, haptic interface, human robot collaboration, man machine interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2013b,

title = {Motion constraint satisfaction by means of closed form solution for redundant robot arms},

author = {Ioannis Iossifidis},

doi = {10.1109/ROBIO.2013.6739780},

isbn = {978-1-4799-2744-9},

year  = {2013},

date = {2013-01-01},

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

pages = {2106--2111},

abstract = {Generation of flexible goal directed movement describes the key skill of autonomous articulated robots. Critical points are still the acknowledgement of reaching and grasping task while satisfying static and dynamically changing constraints given by the environment or caused by the human operator in a collaborative situation. This means that the motion planning dynamics has to incorporate multiple contributions of different qualities which should be formulated in constraint specific reference frames and then transformed into the frame of joint velocities. Whereby the handling of the contribution to motion planning is determined by the solution of the inverse kinematics problem. In this work a closed form solution for the inverse kinematics problem for an eight degree of freedom arm is presented. The geometrical properties of the multi redundant arm and the resulting free parameter which determine it's null space motion are utilized to satisfy constraints of the desired motion. We implement this system on an eight DoF redundant manipulator and show its feasibility in a simulation. textcopyright 2013 IEEE.},

keywords = {Autonomous robotics, Inverse kinematics, motion constraints, redundant robot},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2013d,

title = {Utilizing Artificial Skin for Direct Physical Interaction},

author = {Ioannis Iossifidis},

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 = {Autonomous robotics, direct physical interaction, haptic interface, human robot collaboration, man machine interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2013db,

title = {Motion Constraint Satisfaction by Means of Closed Form Solution for Redundant Robot Arms},

author = {Ioannis Iossifidis},

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 = {Autonomous robotics, Inverse kinematics, motion constraints, redundant robot},

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{Iossifidis2013f,

title = {Utilizing Artificial Skin for Direct Physical Interaction},

author = {Ioannis Iossifidis},

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 = {Autonomous robotics, direct physical interaction, haptic interface, human robot collaboration, man machine interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2013ab,

title = {Motion Constraint Satisfaction by Means of Closed Form Solution for Redundant Robot Arms},

author = {Ioannis Iossifidis},

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 = {Autonomous robotics, Inverse kinematics, motion constraints, redundant robot},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2013bb,

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 = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Noth2012a,

title = {An integrated architecture for the development and assessment of ADAS},

author = {S Noth and J Edelbrunner and I Iossifidis},

doi = {10.1109/ITSC.2012.6338805},

isbn = {9781467330640},

year  = {2012},

date = {2012-01-01},

booktitle = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},

abstract = {Advanced Driver Assistant Systems act, by definition in natural, often poorly structured, environments and are supposed to closely interact with human operators. Both, natural environments as well as human behaviour have no inherent metric and can not be modelled/measured in the classical way physically plausibly behaving systems are described. textcopyright 2012 IEEE.},

keywords = {ADAS, Autonomous Driving, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Noth2012b,

title = {A Versatile Simulated Reality Framework: From Embedded Components to ADAS},

author = {Sebastian Noth and Johann Edelbrunner and Ioannis Iossifidis},

year  = {2012},

date = {2012-01-01},

booktitle = {International Conference on Pervasive and Embedded and Communication Systems, 2012, PECCS2012},

keywords = {Autonomous robotics, Machine Learning, simulated reality, Simulation, virtual reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Dogan2011a,

title = {Autonomous driving: A comparison of machine learning techniques by means of the prediction of lane change behavior},

author = {Ürün Dogan and Johann Edelbrunner and Ioannis Iossifidis},

doi = {10.1109/ROBIO.2011.6181557},

issn = {01962892},

year  = {2011},

date = {2011-01-01},

booktitle = {2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011},

pages = {1837--1843},

abstract = {In the presented work we compare machine learning techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best feature combination, and the most appropriate machine learning technique for the described task. Based on the data acquired from human drivers in the traffic simulator NISYS TRS1, we trained a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able to predict lane changes up to 1.5 sec in beforehand.},

keywords = {ADAS, Autonomous Driving, lane change prediction, Machine Learning},

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{Dogan2011b,

title = {Autonomous Driving: A Comparison of Machine Learning Techniques by Measns of the Prediction of Lane Change Behavior},

author = {Urun Dogan and Johann Edelbrunner and Ioannis Iossifidis},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

booktitle = {Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011)},

keywords = {driver information systems, feed forward neural network, lane change maneuvers, Machine Learning, recurrent neural network, support vector machines, traffic simulator},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Noth2011,

title = {Simulated reality environment for development and assessment of cognitive robotic systems},

author = {Sebastian Noth and Ioannis Iossifidis},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

booktitle = {Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011)},

abstract = {Simulated reality environment incorporating humans and physically plausible behaving robots, providing natural interaction channels, with the option to link simulator to real perception and motion, is gaining importance for the development of cognitive, intuitive interacting and collaborating robotic systems. 

 

 In the present work we introduce a head tracking system which is utilized to incorporate human ego motion in simulated environment improving immersion in the context of human-robot collaborative tasks.},

keywords = {Autonomous robotics, Machine Learning, Simulation, virtual reality},

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{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}

}

@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{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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@inproceedings{Noth2010,

title = {Using ego motion feedback to improve the immersion in virtual reality environments},

author = {Sebastian Noth and Eva Schrowangen and Ioannis Iossifidis},

year  = {2010},

date = {2010-01-01},

urldate = {2010-01-01},

booktitle = {ISR / ROBOTIK 2010},

address = {Munich, Germany},

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 = {head tracking, Simulation, virtual reality},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@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}

}

@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}

}

@inproceedings{Dogan2008b,

title = {Towards a Driver Model: Preliminary Study of Lane Change Behavior},

author = {Ueruen Dogan and Hannes Edelbrunner and Ioannis Iossifidis},

url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4732700},

doi = {10.1109/ITSC.2008.4732700},

isbn = {978-1-4244-2111-4},

year  = {2008},

date = {2008-10-01},

booktitle = {2008 11th International IEEE Conference on Intelligent Transportation Systems},

pages = {931--937},

publisher = {IEEE},

abstract = {The presented work formulates an framework in which early prediction of drivers lane change behavior is realized. We aim to build a representation of drivers lane change behavior in order to recognize and to predict driver's intentions as a first step towards a realistic driver model. In the test bed of the Institute of Neuroinformatik, based on the traffic simulator NISYS TRS 1, 10 individuals have driven in the experiments and they performed more then 150 lane change maneuvers. Lane-offset, distance to the front car and time to contact, were recorded. The acquired data was used to train - in parallel- a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able of performing a lane change prediction time of 1.5 sec beforehand. The proposed approach describes a framework for lane-change detection and prediction, which will serve as a prerequisite for a successful driver model.},

keywords = {driver information systems, driver model, drivers lane change behavior prediction, feed forward neural network, feedforward neural nets, lane change maneuvers, Machine Learning, recurrent neural nets, recurrent neural network, support vector machines, traffic simulator},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Dogan2008,

title = {Towards a Driver Model: Preliminary Study of Lane Change Behavior},

author = {Urun Dogan and Johann Edelbrunner and Ioannis Iossifidis},

doi = {10.1109/ITSC.2008.4732700},

year  = {2008},

date = {2008-01-01},

urldate = {2008-01-01},

booktitle = {Proc. 11th International IEEE Conference on Intelligent Transportation Systems ITSC 2008},

pages = {931--937},

address = {Beijing, China},

abstract = {The presented work formulates an framework in which early prediction of drivers lane change behavior is realized. We aim to build a representation of drivers lane change behavior in order to recognize and to predict driver's intentions as a first step towards a realistic driver model. In the test bed of the Institute of Neuroinformatik, based on the traffic simulator NISYS TRS textlesssuptextgreater1textless/suptextgreater, 10 individuals have driven in the experiments and they performed more then 150 lane change maneuvers. Lane-offset, distance to the front car and time to contact, were recorded. The acquired data was used to train - in parallel- a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able of performing a lane change prediction time of 1.5 sec beforehand. The proposed approach describes a framework for lane-change detection and prediction, which will serve as a prerequisite for a successful driver model.},

keywords = {driver information systems, driver model, drivers lane change behavior prediction, feed forward neural network, feedforward neural nets, lane change maneuvers, Machine Learning, recurrent neural nets, recurrent neural network, support vector machines, traffic simulator},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2006a,

title = {Dynamical Systems Approach for the Autonomous Avoidance of Obstacles and Joint-limits for an Redundant Robot Arm},

author = {Ioannis Iossifidis and Gregor Schöner and Gregor Schoner},

doi = {10.1109/IROS.2006.282468},

isbn = {1-4244-0258-1},

year  = {2006},

date = {2006-10-01},

booktitle = {2006 IEEE/RSJ International Conference on Intelligent Robots and Systems},

pages = {580--585},

publisher = {IEEE},

abstract = {We extend the attractor dynamics approach to generate goal-directed movement of a redundant, anthropomorphic arm while avoiding dynamic obstacles and respecting joint limits. To make the robot's movements human-like, we generate approximately straight-line trajectories by using two heading direction angles of the tool-point quite analogously to how movement is 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. A fifth equation governs the redundancy angle, which controls the elevation of the elbow so as to avoid obstacles and respect joint limits. These variables make it possible to generate movement while sitting in an attractor (or, in the language of the potential field approach, in a minimum). We demonstrate the approach on an assistant robot, which interacts with human users in a shared workspace},

keywords = {attractor dynamics approach, collision avoidance, manipulator dynamics, obstacle avoidance, primate central nervous system, redundant robot arm, telerobotics},

pubstate = {published},

tppubtype = {inproceedings}

}