Prof. Dr. rer. nat. Ioannis Iossifidis

@inproceedings{Rano2013,

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

author = {Inaki Rano and Ioannis Iossifidis},

doi = {10.1109/ROBIO.2013.6739777},

isbn = {9781479927449},

year  = {2013},

date = {2013-01-01},

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

pages = {2088--2093},

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

keywords = {arm movement model, Autonomous robotics, Dynamical systems, movement model},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2013a,

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

author = {Ioannis Iossifidis and Ianki Rano},

year  = {2013},

date = {2013-01-01},

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

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

 

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

keywords = {arm movement model, Autonomous robotics, Dynamical systems, movement model},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {arm movement model, Autonomous robotics, behavior generation, Dynamical systems, movement model, obstacle avoidance},

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 = {arm movement model, Autonomous robotics, behavior generation, Dynamical systems, man machine interaction, movement model, speech recognition},

pubstate = {published},

tppubtype = {inproceedings}

}