Lehrgebiet: Theoretische Informatik und künstliche Intelligenz
Büro: 01.214
Labor: 04.105
Telefon: +49 208 88254-806
E-Mail:
Ioannis Iossifidis studierte Physik (Schwerpunkt: theoretische Teilchenphysik) an der Universität Dortmund und promovierte 2006 an der Fakultät für Physik und Astronomie der Ruhr-Universität Bochum.
Am Institut für Neuroinformatik leitete Prof. Dr. Iossifidis die Arbeitsgruppe Autonome Robotik und nahm mit seiner Forschungsgruppe erfolgreich an zahlreichen, vom BmBF und der EU, geförderten Forschungsprojekten aus dem Bereich der künstlichen Intelligenz teil. Seit dem 1. Oktober 2010 arbeitet er an der HRW am Institut Informatik und hält den Lehrstuhl für Theoretische Informatik – Künstliche Intelligenz.
Prof. Dr. Ioannis Iossifidis entwickelt seit über 20 Jahren biologisch inspirierte anthropomorphe, autonome Robotersysteme, die zugleich Teil und Ergebnis seiner Forschung im Bereich der rechnergestützten Neurowissenschaften sind. In diesem Rahmen entwickelte er Modelle zur Informationsverarbeitung im menschlichen Gehirn und wendete diese auf technische Systeme an.
Ausgewiesene Schwerpunkte seiner wissenschaftlichen Arbeit der letzten Jahre sind die Modellierung menschlicher Armbewegungen, der Entwurf von sogenannten «Simulierten Realitäten» zur Simulation und Evaluation der Interaktionen zwischen Mensch, Maschine und Umwelt sowie die Entwicklung von kortikalen exoprothetischen Komponenten. Entwicklung der Theorie und Anwendung von Algorithmen des maschinellen Lernens auf Basis tiefer neuronaler Architekturen bilden das Querschnittsthema seiner Forschung.
Ioannis Iossifidis’ Forschung wurde u.a. mit Fördermitteln im Rahmen großer Förderprojekte des BmBF (NEUROS, MORPHA, LOKI, DESIRE, Bernstein Fokus: Neuronale Grundlagen des Lernens etc.), der DFG («Motor‐parietal cortical neuroprosthesis with somatosensory feedback for restoring hand and arm functions in tetraplegic patients») und der EU (Neural Dynamics – EU (STREP), EUCogII, EUCogIII ) honoriert und gehört zu den Gewinnern der Leitmarktwettbewerbe Gesundheit.NRW und IKT.NRW 2019.
ARBEITS- UND FORSCHUNGSSCHWERPUNKTE
- Computational Neuroscience
- Brain Computer Interfaces
- Entwicklung kortikaler exoprothetischer Komponenten
- Theorie neuronaler Netze
- Modellierung menschlicher Armbewegungen
- Simulierte Realität
WISSENSCHAFTLICHE EINRICHTUNGEN
- Labor mit Verlinkung
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LEHRVERANSTALTUNGEN
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PROJEKTE
- Projekt mit Verlinkung
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WISSENSCHAFTLICHE MITARBEITER*INNEN
Felix Grün
Büro: 02.216 (Campus Bottrop)
Marie Schmidt
Büro: 02.216 (Campus Bottrop)
Aline Xavier Fidencio
Gastwissenschaftlerin
Muhammad Ayaz Hussain
Doktorand
Tim Sziburis
Doktorand
Farhad Rahmat
studentische Hilfskraft
AUSGEWÄHLTE PUBLIKATIONEN
-
2013
11.Iossifidis, Ioannis; Rano, Ianki
Modeling Human Arm Motion by Means of Attractor Dynamics Approach Proceedings Article
In: Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2013), 2013.
Abstract | BibTeX | Schlagwörter: arm movement model, Autonomous robotics, Dynamical systems, movement model
@inproceedings{Iossifidis2013a,
title = {Modeling Human Arm Motion by Means of Attractor Dynamics Approach},
author = {Ioannis Iossifidis and Ianki Rano},
year = {2013},
date = {2013-01-01},
booktitle = {Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2013)},
abstract = {Autonomous robots with limited computational capacity call for control approaches that generate meaningful, goal-directed behavior without using a large amount of resources. The attractor dynamics approach to movement generation is a framework that links sensor data to motor commands via coupled dynamical systems that have attractors at behaviorally desired states. The low computational demands leave enough system resources for higher level function like forming a sequence of local goals to reach a distant one. The comparatively high performance of local behavior generation allows the global planning to be relatively simple.
In the present paper, we apply this approach to generate walking trajectories for a small humanoid robot, the Aldebaran Nao, that are goal-directed and avoid obstacles. The sensor information is a single camera in the head of the robot. The limited field of vision is compensated by head movements. The design of the dynamical system for motion generation and the choice of state variable makes a computationally expensive scene representation or local map building unnecessary.},
keywords = {arm movement model, Autonomous robotics, Dynamical systems, movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
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.2010
10.Reimann, Hendrik; Iossifidis, Ioannis; Schöner, Gregor
End-effector obstacle avoidance using multiple dynamic variables Proceedings Article
In: ISR / ROBOTIK 2010, Munich, Germany, 2010.
Abstract | BibTeX | Schlagwörter: arm movement model, Autonomous robotics, behavior generation, Dynamical systems, movement model, obstacle avoidance
@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}
}
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.9.Grimm, Matthias; Iossifidis, Ioannis
Behavioral Organization for Mobile Robotic Systems: An Attractor Dynamics Approach Proceedings Article
In: ISR / ROBOTIK 2010, Munich, Germany, 2010.
Abstract | BibTeX | Schlagwörter: Autonomous robotics, behavior generation, Dynamical systems, movement model, movile robot
@inproceedings{Grimm2010b,
title = {Behavioral Organization for Mobile Robotic Systems: An Attractor Dynamics Approach},
author = {Matthias Grimm and Ioannis Iossifidis},
year = {2010},
date = {2010-01-01},
booktitle = {ISR / ROBOTIK 2010},
address = {Munich, Germany},
abstract = {Autonomous systems generate different behaviors based on the perceived environmental situation. The organization of a set of behaviors plays an important role in the field of autonomous robotics. The organization architecture must be flexible, so that behavioral changes are possible if the sensory information changes. Furthermore, behavioral organization must be stable, so that small changes in sensory information do not lead to oscillations. To achieve this, all behaviors, but also the underlying organization architecture, are based on continuous dynamical systems. They are characterized by a set of dynamical variables, also referred to as state variables. These variables represent the activation or deactivation of a particular behavior. Elementary behaviors are dependent on the sensor input in a way, that changes of the sensorial information lead to qualitatively different behaviors. The so-called sensor context denotes whether a behavior is applicable in the current sensor situation or not. However, for complex systems consisting of many elementary behaviors, it is necessary to take logical conditions into account to generate a sequence of behaviors. Furthermore, some elementary behaviors can or even must run in parallel, while others exclude each other. This internal information requires knowledge about the logical interaction of the behaviors and is stored within binary matrices. This makes the overall organization structure very flexible and easy to extend. We present the architecture using the example of approaching and passing a door. The robot has to navigate from one room to another while simultaneously avoiding obstacles in its pathway.},
keywords = {Autonomous robotics, behavior generation, Dynamical systems, movement model, movile robot},
pubstate = {published},
tppubtype = {inproceedings}
}
Autonomous systems generate different behaviors based on the perceived environmental situation. The organization of a set of behaviors plays an important role in the field of autonomous robotics. The organization architecture must be flexible, so that behavioral changes are possible if the sensory information changes. Furthermore, behavioral organization must be stable, so that small changes in sensory information do not lead to oscillations. To achieve this, all behaviors, but also the underlying organization architecture, are based on continuous dynamical systems. They are characterized by a set of dynamical variables, also referred to as state variables. These variables represent the activation or deactivation of a particular behavior. Elementary behaviors are dependent on the sensor input in a way, that changes of the sensorial information lead to qualitatively different behaviors. The so-called sensor context denotes whether a behavior is applicable in the current sensor situation or not. However, for complex systems consisting of many elementary behaviors, it is necessary to take logical conditions into account to generate a sequence of behaviors. Furthermore, some elementary behaviors can or even must run in parallel, while others exclude each other. This internal information requires knowledge about the logical interaction of the behaviors and is stored within binary matrices. This makes the overall organization structure very flexible and easy to extend. We present the architecture using the example of approaching and passing a door. The robot has to navigate from one room to another while simultaneously avoiding obstacles in its pathway.8.Sandamirskaya, Yulia; Lipinski, John; Iossifidis, Ioannis; Schöner, G
Natural human-robot interaction through spatial language: a dynamic neural fields approach Proceedings Article
In: Proc. 19th IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2010), S. 600–607, IEEE, 2010, ISSN: 1944-9445.
Links | BibTeX | Schlagwörter: arm movement model, Autonomous robotics, behavior generation, Dynamical systems, man machine interaction, movement model, speech recognition
@inproceedings{Sandamirskayasubmitted,
title = {Natural human-robot interaction through spatial language: a dynamic neural fields approach},
author = {Yulia Sandamirskaya and John Lipinski and Ioannis Iossifidis and G Schöner},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5598671},
issn = {1944-9445},
year = {2010},
date = {2010-01-01},
booktitle = {Proc. 19th IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2010)},
pages = {600--607},
publisher = {IEEE},
keywords = {arm movement model, Autonomous robotics, behavior generation, Dynamical systems, man machine interaction, movement model, speech recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
2005
7.Iossifidis, Ioannis; Steinhage, A
Behavior generation for Anthropomorphic robots by means of dynamical systems Buch
2005, ISSN: 16107438.
Abstract | BibTeX | Schlagwörter: arm movement model, Autonomous robotics, behavior generation, Dynamical systems, movement model
@book{Iossifidis2005c,
title = {Behavior generation for Anthropomorphic robots by means of dynamical systems},
author = {Ioannis Iossifidis and A Steinhage},
issn = {16107438},
year = {2005},
date = {2005-01-01},
urldate = {2005-01-01},
booktitle = {Springer Tracts in Advanced Robotics},
volume = {14},
abstract = {This article describes the current state of our research on anthropomorphic robots. Our aim is to make the reader familiar with the two basic principles our work is based on: anthropomorphism and dynamics. The principle of anthropomorphism means a restriction to human-like robots which use version, audition and touch as their only sensors so that natural man-machine interaction is possible. The principle of dynamics stands for the mathematical framework based on which our robots generate their behavior. Both principles have their root in the idea that concepts of biological behavior and information processing can be exploited to control technical systems. textcopyright Springer-Verlag Berlin Heidelberg 2005.},
keywords = {arm movement model, Autonomous robotics, behavior generation, Dynamical systems, movement model},
pubstate = {published},
tppubtype = {book}
}
This article describes the current state of our research on anthropomorphic robots. Our aim is to make the reader familiar with the two basic principles our work is based on: anthropomorphism and dynamics. The principle of anthropomorphism means a restriction to human-like robots which use version, audition and touch as their only sensors so that natural man-machine interaction is possible. The principle of dynamics stands for the mathematical framework based on which our robots generate their behavior. Both principles have their root in the idea that concepts of biological behavior and information processing can be exploited to control technical systems. textcopyright Springer-Verlag Berlin Heidelberg 2005.2004
6.Prassler, Erwin; Lawitzky, Gisbert; Stopp, Andreas; Grunwald, Gerhard; Ħägele, Martin; Đillmann, Rüdiger; Iossifidis, Ioannis
Advances in Ħuman Robot Interaction Buch
Springer Press, 2004.
Abstract | Links | BibTeX | Schlagwörter: arm movement model, Autonomous robotics, behavior generation, Dynamical systems, movement model
@book{Prassler2004,
title = {Advances in Ħuman Robot Interaction},
author = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Ħägele and Rüdiger Đillmann and Ioannis Iossifidis},
editor = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Ħägele and Rüdiger Đillmann and Ioannis Iossifidis},
url = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-102-22-35029562-0,00.html?changeHeader=true},
year = {2004},
date = {2004-01-01},
booktitle = {Advances in Ħuman Robot Interaction},
volume = {14/2004},
number = {ISBN: 3-540-23211-7},
pages = {414},
publisher = {Springer Press},
series = {Springer Tracts in Advanced Robotics STAR},
abstract = {Human Robot Interaction and Cooperation
Motion Coordination
Multi-Modal Robot Interfaces
Physical Interaction between Humans and Robots
Robot Learning
Visual Instruction of Robots},
keywords = {arm movement model, Autonomous robotics, behavior generation, Dynamical systems, movement model},
pubstate = {published},
tppubtype = {book}
}
Human Robot Interaction and Cooperation
Motion Coordination
Multi-Modal Robot Interfaces
Physical Interaction between Humans and Robots
Robot Learning
Visual Instruction of Robots5.Iossifidis, Ioannis; Schöner, Gregor
Attractor dynamics approach for autonomous collision-free path generation in 3d-space for an 7 dof robot arm Proceedings Article
In: Proceedings of the ROBOTIK 2004, Leistungsstand - Anwendungen - Visionen - Trends, number 1841 in VDI-Berichte, S. 815–822, VDI/VDE VDI Verlag, München, Germany, 2004.
BibTeX | Schlagwörter: arm movement model, Autonomous robotics, collision avoidance, Dynamical systems, inverse kinematics, movement model
@inproceedings{Iossifidis2004a,
title = {Attractor dynamics approach for autonomous collision-free path generation in 3d-space for an 7 dof robot arm},
author = {Ioannis Iossifidis and Gregor Schöner},
year = {2004},
date = {2004-01-01},
booktitle = {Proceedings of the ROBOTIK 2004, Leistungsstand - Anwendungen - Visionen - Trends, number 1841 in VDI-Berichte},
pages = {815--822},
publisher = {VDI Verlag},
address = {München, Germany},
organization = {VDI/VDE},
keywords = {arm movement model, Autonomous robotics, collision avoidance, Dynamical systems, inverse kinematics, movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
4.Iossifidis, Ioannis; Bruckhoff, Carsten; Theis, Christoph; Grote, Claudia; Faubel, Christian; Schöner, Gregor
A Cooperative Robot Assistant CoRA For Human Environments Buchabschnitt
In: Prassler, Erwin; Lawitzky, Gisbert; Stopp, Andreas; Grunwald, Gerhard; Hägele, Martin; Dillmann, Rüdiger; Iossifidis, Ioannis (Hrsg.): Advances in Human Robot Interaction, Bd. 14/2004, Nr. ISBN: 3-540-23211-7,, S. 385–401, Springer Press, 2004, ISBN: 3-540-23211-7,.
Abstract | Links | BibTeX | Schlagwörter: arm movement model, Autonomous robotics, behavior generation, Dynamical systems, movement model
@incollection{Iossifidis2004d,
title = {A Cooperative Robot Assistant CoRA For Human Environments},
author = {Ioannis Iossifidis and Carsten Bruckhoff and Christoph Theis and Claudia Grote and Christian Faubel and Gregor Schöner},
editor = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Hägele and Rüdiger Dillmann and Ioannis Iossifidis},
url = {http://www.springerlink.com/index/91656F7B99CD2C2C},
doi = {10.1007/b97960},
isbn = {3-540-23211-7,},
year = {2004},
date = {2004-01-01},
booktitle = {Advances in Human Robot Interaction},
volume = {14/2004},
number = {ISBN: 3-540-23211-7,},
pages = {385--401},
publisher = {Springer Press},
chapter = {7},
series = {Springer Tracts in Advanced Robotics STAR},
abstract = {CoRA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand-over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robotrsquos gripper (force sensing). The design objective has been to exploit the human operatorrsquos intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.},
keywords = {arm movement model, Autonomous robotics, behavior generation, Dynamical systems, movement model},
pubstate = {published},
tppubtype = {incollection}
}
CoRA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand-over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robotrsquos gripper (force sensing). The design objective has been to exploit the human operatorrsquos intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.3.Iossifidis, Ioannis; Steinhage, Axel
Behavior Generation For Anthropomorphic Robots by Means of Dynamical Systems Buchabschnitt
In: Prassler, Erwin; Lawitzky, Gisbert; Stopp, Andreas; Grunwald, Gerhard; Hägele, Martin; Dillmann, Rüdiger; Iossifidis, Ioannis (Hrsg.): Advances in Human Robot Interaction, Bd. 14/2004, Nr. ISBN: 3-540-23211-7,, S. 269–300, Springer Press, 2004, ISBN: 3-540-23211-7,.
Abstract | Links | BibTeX | Schlagwörter: arm movement model, Autonomous robotics, behavior generation, Dynamical systems, movement model
@incollection{Iossifidis2004e,
title = {Behavior Generation For Anthropomorphic Robots by Means of Dynamical Systems},
author = {Ioannis Iossifidis and Axel Steinhage},
editor = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Hägele and Rüdiger Dillmann and Ioannis Iossifidis},
url = {http://www.springerlink.com/index/96DD6AB012CF71E7},
doi = {0.1007/b97960},
isbn = {3-540-23211-7,},
year = {2004},
date = {2004-01-01},
booktitle = {Advances in Human Robot Interaction},
volume = {14/2004},
number = {ISBN: 3-540-23211-7,},
pages = {269--300},
publisher = {Springer Press},
chapter = {6},
series = {Springer Tracts in Advanced Robotics STAR},
abstract = {This article describes the current state of our research on anthropomorphic robots. Our aim is to make the reader familiar with the two basic principles our work is based on: anthropomorphism and dynamics. The principle of anthropomorphism means a restriction to human-like robots which use version, audition and touch as their only sensors so that natural man-machine interaction is possible. The principle of dynamics stands for the mathematical framework based on which our robots generate their behavior. Both principles have their root in the idea that concepts of biological behavior and information processing can be exploited to control technical systems.},
keywords = {arm movement model, Autonomous robotics, behavior generation, Dynamical systems, movement model},
pubstate = {published},
tppubtype = {incollection}
}
This article describes the current state of our research on anthropomorphic robots. Our aim is to make the reader familiar with the two basic principles our work is based on: anthropomorphism and dynamics. The principle of anthropomorphism means a restriction to human-like robots which use version, audition and touch as their only sensors so that natural man-machine interaction is possible. The principle of dynamics stands for the mathematical framework based on which our robots generate their behavior. Both principles have their root in the idea that concepts of biological behavior and information processing can be exploited to control technical systems.2.Prassler, Erwin; Lawitzky, Gisbert; Stopp, Andreas; Grunwald, Gerhard; Hägele, Martin; Dillmann, Rüdiger; Iossifidis, Ioannis
Advances in Human Robot Interaction Buch
Springer Press, 2004.
Abstract | Links | BibTeX | Schlagwörter: arm movement model, Autonomous robotics, behavior generation, Dynamical systems, inverse kinematics, movement model, redundant robot arm
@book{Prassler2004b,
title = {Advances in Human Robot Interaction},
author = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Hägele and Rüdiger Dillmann and Ioannis Iossifidis},
editor = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Hägele and Rüdiger Dillmann and Ioannis Iossifidis},
url = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-102-22-35029562-0,00.html?changeHeader=true},
year = {2004},
date = {2004-01-01},
booktitle = {Advances in Human Robot Interaction},
volume = {14/2004},
pages = {414},
publisher = {Springer Press},
series = {Springer Tracts in Advanced Robotics STAR},
abstract = {Human Robot Interaction and Cooperation Motion Coordination Multi-Modal Robot Interfaces Physical Interaction between Humans and Robots Robot Learning Visual Instruction of Robots},
keywords = {arm movement model, Autonomous robotics, behavior generation, Dynamical systems, inverse kinematics, movement model, redundant robot arm},
pubstate = {published},
tppubtype = {book}
}
Human Robot Interaction and Cooperation Motion Coordination Multi-Modal Robot Interfaces Physical Interaction between Humans and Robots Robot Learning Visual Instruction of Robots