Publications
2022
40.
Marie Dominique Schmidt; Tobias Glasmachers; Ioannis Iossifidis
From Motion to Muscle Journal Article
In: arXiv: 2201.11501 [cs.LG], 2022.
Links | BibTeX | Tags: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@article{schmidt2022motion,
title = {From Motion to Muscle},
author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
doi = {https://doi.org/10.48550/arXiv.2201.11501},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {arXiv: 2201.11501 [cs.LG]},
keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},
pubstate = {published},
tppubtype = {article}
}
39.
Marie Dominique Schmidt; Tobias Glasmachers; Ioannis Iossifidis
Motion Intention Prediction Inproceedings
In: FENS, Forum 2022, FENS, Federation of European Neuroscience Societies, 2022.
Abstract | BibTeX | Tags: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@inproceedings{schmidtMotionIntentionPrediction2022a,
title = {Motion Intention Prediction},
author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {FENS, Forum 2022},
publisher = {FENS, Federation of European Neuroscience Societies},
abstract = {Motion intention prediction is the key to robot-assisted rehabilitation systems. These can rely on various biological signals. One commonly used signal is the muscle activity measured by an electromyogram that occurs between 50-100 milliseconds before the actual movement, allowing a real-world application to assist in time. We show that upper limb motion can be estimated from the corresponding muscle activity. To this end, eight-arm muscles are mapped to the joint angle, velocity, and acceleration of the shoulder, elbow, and wrist. For this purpose, we specifically develop an artificial neural network that estimates complex motions involving multiple upper limb joints. The network model is evaluated concerning its ability to generalize across subjects as well as for new motions. This is achieved through training on multiple subjects and additional transfer learning methods so that the prediction for new subjects is significantly improved. In particular, this is beneficial for a robust real-world application. Furthermore, we investigate the importance of the different parameters such as angle, velocity, and acceleration for simple and complex motions. Predictions for simple motions along with the main components of complex motions achieve excellent accuracy while joints that do not play a dominant role during the motion have comparatively lower accuracy.},
keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},
pubstate = {published},
tppubtype = {inproceedings}
}
Motion intention prediction is the key to robot-assisted rehabilitation systems. These can rely on various biological signals. One commonly used signal is the muscle activity measured by an electromyogram that occurs between 50-100 milliseconds before the actual movement, allowing a real-world application to assist in time. We show that upper limb motion can be estimated from the corresponding muscle activity. To this end, eight-arm muscles are mapped to the joint angle, velocity, and acceleration of the shoulder, elbow, and wrist. For this purpose, we specifically develop an artificial neural network that estimates complex motions involving multiple upper limb joints. The network model is evaluated concerning its ability to generalize across subjects as well as for new motions. This is achieved through training on multiple subjects and additional transfer learning methods so that the prediction for new subjects is significantly improved. In particular, this is beneficial for a robust real-world application. Furthermore, we investigate the importance of the different parameters such as angle, velocity, and acceleration for simple and complex motions. Predictions for simple motions along with the main components of complex motions achieve excellent accuracy while joints that do not play a dominant role during the motion have comparatively lower accuracy.
38.
Marie Dominique Schmidt; Tobias Glasmachers; Ioannis Iossifidis
From Motion to Muscle Journal Article
In: arXiv: 2201.11501 [cs.LG], 2022.
Links | BibTeX | Tags: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@article{schmidt2022motionb,
title = {From Motion to Muscle},
author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
doi = {https://doi.org/10.48550/arXiv.2201.11501},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {arXiv: 2201.11501 [cs.LG]},
keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},
pubstate = {published},
tppubtype = {article}
}
37.
Marie Dominique Schmidt; Tobias Glasmachers; Ioannis Iossifidis
Motion Intention Prediction Inproceedings
In: FENS, Forum 2022, FENS, Federation of European Neuroscience Societies, 2022.
Abstract | BibTeX | Tags: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@inproceedings{schmidtMotionIntentionPrediction2022ab,
title = {Motion Intention Prediction},
author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {FENS, Forum 2022},
publisher = {FENS, Federation of European Neuroscience Societies},
abstract = {Motion intention prediction is the key to robot-assisted rehabilitation systems. These can rely on various biological signals. One commonly used signal is the muscle activity measured by an electromyogram that occurs between 50-100 milliseconds before the actual movement, allowing a real-world application to assist in time. We show that upper limb motion can be estimated from the corresponding muscle activity. To this end, eight-arm muscles are mapped to the joint angle, velocity, and acceleration of the shoulder, elbow, and wrist. For this purpose, we specifically develop an artificial neural network that estimates complex motions involving multiple upper limb joints. The network model is evaluated concerning its ability to generalize across subjects as well as for new motions. This is achieved through training on multiple subjects and additional transfer learning methods so that the prediction for new subjects is significantly improved. In particular, this is beneficial for a robust real-world application. Furthermore, we investigate the importance of the different parameters such as angle, velocity, and acceleration for simple and complex motions. Predictions for simple motions along with the main components of complex motions achieve excellent accuracy while joints that do not play a dominant role during the motion have comparatively lower accuracy.},
keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},
pubstate = {published},
tppubtype = {inproceedings}
}
Motion intention prediction is the key to robot-assisted rehabilitation systems. These can rely on various biological signals. One commonly used signal is the muscle activity measured by an electromyogram that occurs between 50-100 milliseconds before the actual movement, allowing a real-world application to assist in time. We show that upper limb motion can be estimated from the corresponding muscle activity. To this end, eight-arm muscles are mapped to the joint angle, velocity, and acceleration of the shoulder, elbow, and wrist. For this purpose, we specifically develop an artificial neural network that estimates complex motions involving multiple upper limb joints. The network model is evaluated concerning its ability to generalize across subjects as well as for new motions. This is achieved through training on multiple subjects and additional transfer learning methods so that the prediction for new subjects is significantly improved. In particular, this is beneficial for a robust real-world application. Furthermore, we investigate the importance of the different parameters such as angle, velocity, and acceleration for simple and complex motions. Predictions for simple motions along with the main components of complex motions achieve excellent accuracy while joints that do not play a dominant role during the motion have comparatively lower accuracy.
2021
36.
Marie Dominique Schmidt; Tobias Glasmachers; Ioannis Iossifidis
Artificially Generated Muscle Signals Inproceedings
In: Bernstein Conference, 2021.
Links | BibTeX | Tags: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@inproceedings{schmidtArtificiallyGeneratedMuscle2021b,
title = {Artificially Generated Muscle Signals},
author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2021.p111},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
publisher = {Bernstein Conference},
keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},
pubstate = {published},
tppubtype = {inproceedings}
}
35.
Marie Dominique Schmidt; Tobias Glasmachers; Ioannis Iossifidis
Artificially Generated Muscle Signals Inproceedings
In: Bernstein Conference, 2021.
Links | BibTeX | Tags: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@inproceedings{schmidtArtificiallyGeneratedMuscle2021bb,
title = {Artificially Generated Muscle Signals},
author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2021.p111},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
publisher = {Bernstein Conference},
keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},
pubstate = {published},
tppubtype = {inproceedings}
}
34.
Tim Sziburis; Susanne Blex; Tobias Glasmachers; Inaki Rano; Ioannis Iossifidis
Modelling the Generation of Human Upper-Limb Reaching Trajectories: An Extended Behavioural Attractor Dynamics Approach Inproceedings
In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.
Links | BibTeX | Tags: BCI, Machine Learning, movement model
@inproceedings{sziburisModellingGenerationHuman2021,
title = {Modelling the Generation of Human Upper-Limb Reaching Trajectories: An Extended Behavioural Attractor Dynamics Approach},
author = {Tim Sziburis and Susanne Blex and Tobias Glasmachers and Inaki Rano and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2021.p078},
year = {2021},
date = {2021-09-15},
urldate = {2021-09-15},
booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {BCI, Machine Learning, movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
33.
Muhammad Ayaz Hussain; Christian Klaes; Ioannis Iossifidis:
Toward a Model of Timed Arm Movement Based on Temporal Tuning of Neurons in Primary Motor (MI) and Posterior Parietal Cortex (PPC) Title Inproceedings
In: BC18 : Computational Neuroscience & Neurotechnology Bernstein Conference 2018, BCCN, 2018.
Abstract | BibTeX | Tags: BCI, dynamical systems, Machine Learning, movement model
@inproceedings{bccn18,
title = {Toward a Model of Timed Arm Movement Based on Temporal Tuning of Neurons in Primary Motor (MI) and Posterior Parietal Cortex (PPC) Title},
author = {Muhammad Ayaz Hussain and Christian Klaes and Ioannis Iossifidis:},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {BC18 : Computational Neuroscience & Neurotechnology Bernstein Conference 2018},
publisher = {BCCN},
abstract = {To study driver behavior we set up a lab with fixed base driving simulators. In order to compensate for the lack of physical feedback in this scenario, we aimed for another means of increasing the realism of our system. In the following, we propose an efficient method of head tracking and its integration in our driving simulation. Furthermore, we illuminate why this is a promising boost of the subjects immersion in the virtual world. Our idea for increasing the feeling of immersion is to give the subject feedback on head movements relative to the screen. A real driver sometimes moves his head in order to see something better or to look behind an occluding object. In addition to these intentional movements, a study conducted by Zirkovitz and Harris has revealed that drivers involuntarily tilt their heads when they go around corners in order to maximize the use of visual information available in the scene. Our system reflects the visual changes of any head movement and hence gives feedback on both involuntary and intentional motion. If, for example, subjects move to the left, they will see more from the right-hand side of the scene. If, on the other hand, they move upwards, a larger fraction of the engine hood will be visible. The same holds for the rear view mirror},
keywords = {BCI, dynamical systems, Machine Learning, movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
To study driver behavior we set up a lab with fixed base driving simulators. In order to compensate for the lack of physical feedback in this scenario, we aimed for another means of increasing the realism of our system. In the following, we propose an efficient method of head tracking and its integration in our driving simulation. Furthermore, we illuminate why this is a promising boost of the subjects immersion in the virtual world. Our idea for increasing the feeling of immersion is to give the subject feedback on head movements relative to the screen. A real driver sometimes moves his head in order to see something better or to look behind an occluding object. In addition to these intentional movements, a study conducted by Zirkovitz and Harris has revealed that drivers involuntarily tilt their heads when they go around corners in order to maximize the use of visual information available in the scene. Our system reflects the visual changes of any head movement and hence gives feedback on both involuntary and intentional motion. If, for example, subjects move to the left, they will see more from the right-hand side of the scene. If, on the other hand, they move upwards, a larger fraction of the engine hood will be visible. The same holds for the rear view mirror
32.
Muhammad Ayaz Hussain; Christian Klaes; Ioannis Iossifidis
Toward a Model of Timed Arm Movement Based on Temporal Tuning of Neurons in Primary Motor (MI) and Posterior Parietal Cortex (PPC) Title Inproceedings
In: BC18 : Computational Neuroscience & Neurotechnology Bernstein Conference 2018, BCCN, 2018.
Abstract | BibTeX | Tags: dynamical systems, movement model, Robotics
@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}
}
To study driver behavior we set up a lab with fixed base driving simulators. In order to compensate for the lack of physical feedback in this scenario, we aimed for another means of increasing the realism of our system. In the following, we propose an efficient method of head tracking and its integration in our driving simulation. Furthermore, we illuminate why this is a promising boost of the subjects immersion in the virtual world. Our idea for increasing the feeling of immersion is to give the subject feedback on head movements relative to the screen. A real driver sometimes moves his head in order to see something better or to look behind an occluding object. In addition to these intentional movements, a study conducted by Zirkovitz and Harris has revealed that drivers involuntarily tilt their heads when they go around corners in order to maximize the use of visual information available in the scene. Our system reflects the visual changes of any head movement and hence gives feedback on both involuntary and intentional motion. If, for example, subjects move to the left, they will see more from the right-hand side of the scene. If, on the other hand, they move upwards, a larger fraction of the engine hood will be visible. The same holds for the rear view mirror
2017
31.
Ioannis Iossifidis:; Muhammad Ayaz Hussain; Christian Klaes
Temporal stabilized arm movement for efficient neuroprosthetic control by individuals with tetraplegia Miscellaneous
2017.
Abstract | BibTeX | Tags: dynamical systems, movement model, neuroprosthetic, Robotics
@misc{Iossifidis2017a,
title = {Temporal stabilized arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
author = {Ioannis Iossifidis: and Muhammad Ayaz Hussain and Christian Klaes},
year = {2017},
date = {2017-01-01},
publisher = {SfN 2017},
abstract = {The generation of discrete movement with distinct and stable time courses characterizes each human movement and reflect the need to perform catching and interception tasks and for timed action sequences, incorporating dynamically changing environmental constraints. Several lines of evidence suggest neuronal mechanism for the initiation of movements i.e. in the supplementary motor area (SMA) and the premotor cortex and for movement planning mechanism generating velocity profiles satisfying time constraints. In order to meet the requirements of on-line evolving trajectories we propose a model, based on dynamical systems which describes goal directed trajectories in humans and generates trajectories for redundant anthropomorphic robotic arms The current study aim to evaluate the temporal characteristics of primary motor and posterior parietal cortex in patients with tetraplegia by using inception task implemented in virtual reality. The participants will be implanted with two 96-channel intracortical microelectrode arrays in the Primary Motor and Post Parietal Cortex. In the training phase the participants will be confronted with the observation of a robotic arm intercepting the bob of a pendulum at the lowest point of it's trajectory (maximum velocity) - the end effector reaches at the same time as the bob of the pendulum the lowest point of the trajectory performing a perfectly timed movement. The arm is positioned perpendicular to the oscillation plane exactly at the hight of the interception point to generate a one dimensional trajectory to the target. The time to contact between the robot's end effector and the bob of the pendulum is maintained constant and during the different sessions the distance between end effector and the point of interception is gradually increased. In order to catch up and to reach in time, either velocity formation or initiation time of the movement have to be changed. Both effects will be investigated independently. For the decoding of movement-related information we introduce a framework exploiting a deep learning approach with a convolutional neural networks.},
keywords = {dynamical systems, movement model, neuroprosthetic, Robotics},
pubstate = {published},
tppubtype = {misc}
}
The generation of discrete movement with distinct and stable time courses characterizes each human movement and reflect the need to perform catching and interception tasks and for timed action sequences, incorporating dynamically changing environmental constraints. Several lines of evidence suggest neuronal mechanism for the initiation of movements i.e. in the supplementary motor area (SMA) and the premotor cortex and for movement planning mechanism generating velocity profiles satisfying time constraints. In order to meet the requirements of on-line evolving trajectories we propose a model, based on dynamical systems which describes goal directed trajectories in humans and generates trajectories for redundant anthropomorphic robotic arms The current study aim to evaluate the temporal characteristics of primary motor and posterior parietal cortex in patients with tetraplegia by using inception task implemented in virtual reality. The participants will be implanted with two 96-channel intracortical microelectrode arrays in the Primary Motor and Post Parietal Cortex. In the training phase the participants will be confronted with the observation of a robotic arm intercepting the bob of a pendulum at the lowest point of it's trajectory (maximum velocity) - the end effector reaches at the same time as the bob of the pendulum the lowest point of the trajectory performing a perfectly timed movement. The arm is positioned perpendicular to the oscillation plane exactly at the hight of the interception point to generate a one dimensional trajectory to the target. The time to contact between the robot's end effector and the bob of the pendulum is maintained constant and during the different sessions the distance between end effector and the point of interception is gradually increased. In order to catch up and to reach in time, either velocity formation or initiation time of the movement have to be changed. Both effects will be investigated independently. For the decoding of movement-related information we introduce a framework exploiting a deep learning approach with a convolutional neural networks.
30.
Ioannis Iossifidis; C Klaes
Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia Miscellaneous
2017.
Abstract | BibTeX | Tags: BCI, dynamical systems, movement model, neuroprosthetic
@misc{Iossifidis2017b,
title = {Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
author = {Ioannis Iossifidis and C Klaes},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
publisher = {SfN 2017},
abstract = {Over the last decades the generation mechanism and the representation of goal- directed movements has been a topic of intensive neurophysiological research. The investigation in the motor, premotor, and parietal areas led to the discovery that the direction of hand's movement in space was encoded by populations of neurons in these areas together with many other movement parameters. These distributions of population activation reflect how movements are prepared ahead of movement initiation, as revealed by activity induced by cues that precede the imperative signal (Georgopoulos, 1991). Inspired by those findings a model based on dynamical systems was proposed both, to model goal directed trajectories in humans and to generate trajectories for redundant anthropomorphic robotic arms. The analysis of the attractor dynamics based on the qualitative comparison with measurements of resulting trajectories taken from arm movement experiments with humans (Grimme u. a., 2012) created a framework able to reproduce and to generate naturalistic human like arm trajectories (Iossifidis und Rano, 2013; Iossifidis, Schöner u. a., 2006). The main idea of the methodology is to choose low-dimensional, behavioral va- riables of the goal task can be represented as attractor states of those variables. The movement is generated through a dynamical system with attractors and repellers on the behavioral space, at the goal and constraint positions respectively. When the motion of the robot evolves according to the dynamics of these systems, the behavioral variables will be stabilized at their attractors. Movement is represented by the polar coordinates $phi$,$theta$ of the movement direction (heading direction) and the angular frequency $ømega$ of a hopf oscillator, generating the velocity profile of the arm movement. Therefore, the system dynamics will be expressed in terms of these variables. The target and each obstacle induce vector fields over these variables in a way that states where the hand is moving closer to the target are attractive, while states where it is moving towards an obstacle are repellant. Contributions from different sources are weighted by different factors, e.g. in the vicinity of an obstacle, the contribution from that obstacle must dominate the behavior to guarantee constraint satisfaction (collision prevention). Based on three parameters the presented framework is able to generate temporal stabilized (timed) discrete movements, dealing with disturbances and maintaining an approximately constant movement time. In the current study we will implant two 96-channel intracortical microelectrode arrays in the primary motor and the posterior parietal cortex (PPC) of an individual with tetraplegia. In the training phase the parameters of the dynamical systems will be tuned and optimized by machine learning algorithms. Rather controlling directly the arm movement and adjusting continuously parameters, the patient adjust by his or hers thoughts the three parameters of the dynamics, which remain almost constant during the movement. Only when the motion plan is changing the parameters have to be readjusted. The target directed trajectory evolves from the attractor solution of the dynamical systems equations, which means that the trajectory is generated while the system is in a stable stationary state, a fixed-point attractor. The increase of the degree of assistance lowers the cognitive load of the patient and enables the acknowledgement of the desired task without frustration. In addition we aim to replace the robotic manipulator by an exoskeleton for the upper body which will enable the patients to move his or hers own limbs, which would complete the development of a real neuroprosthetic device for every day use.},
keywords = {BCI, dynamical systems, movement model, neuroprosthetic},
pubstate = {published},
tppubtype = {misc}
}
Over the last decades the generation mechanism and the representation of goal- directed movements has been a topic of intensive neurophysiological research. The investigation in the motor, premotor, and parietal areas led to the discovery that the direction of hand's movement in space was encoded by populations of neurons in these areas together with many other movement parameters. These distributions of population activation reflect how movements are prepared ahead of movement initiation, as revealed by activity induced by cues that precede the imperative signal (Georgopoulos, 1991). Inspired by those findings a model based on dynamical systems was proposed both, to model goal directed trajectories in humans and to generate trajectories for redundant anthropomorphic robotic arms. The analysis of the attractor dynamics based on the qualitative comparison with measurements of resulting trajectories taken from arm movement experiments with humans (Grimme u. a., 2012) created a framework able to reproduce and to generate naturalistic human like arm trajectories (Iossifidis und Rano, 2013; Iossifidis, Schöner u. a., 2006). The main idea of the methodology is to choose low-dimensional, behavioral va- riables of the goal task can be represented as attractor states of those variables. The movement is generated through a dynamical system with attractors and repellers on the behavioral space, at the goal and constraint positions respectively. When the motion of the robot evolves according to the dynamics of these systems, the behavioral variables will be stabilized at their attractors. Movement is represented by the polar coordinates $phi$,$theta$ of the movement direction (heading direction) and the angular frequency $ømega$ of a hopf oscillator, generating the velocity profile of the arm movement. Therefore, the system dynamics will be expressed in terms of these variables. The target and each obstacle induce vector fields over these variables in a way that states where the hand is moving closer to the target are attractive, while states where it is moving towards an obstacle are repellant. Contributions from different sources are weighted by different factors, e.g. in the vicinity of an obstacle, the contribution from that obstacle must dominate the behavior to guarantee constraint satisfaction (collision prevention). Based on three parameters the presented framework is able to generate temporal stabilized (timed) discrete movements, dealing with disturbances and maintaining an approximately constant movement time. In the current study we will implant two 96-channel intracortical microelectrode arrays in the primary motor and the posterior parietal cortex (PPC) of an individual with tetraplegia. In the training phase the parameters of the dynamical systems will be tuned and optimized by machine learning algorithms. Rather controlling directly the arm movement and adjusting continuously parameters, the patient adjust by his or hers thoughts the three parameters of the dynamics, which remain almost constant during the movement. Only when the motion plan is changing the parameters have to be readjusted. The target directed trajectory evolves from the attractor solution of the dynamical systems equations, which means that the trajectory is generated while the system is in a stable stationary state, a fixed-point attractor. The increase of the degree of assistance lowers the cognitive load of the patient and enables the acknowledgement of the desired task without frustration. In addition we aim to replace the robotic manipulator by an exoskeleton for the upper body which will enable the patients to move his or hers own limbs, which would complete the development of a real neuroprosthetic device for every day use.
29.
Klaes C I. Iossifidis
Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia Conference
SfN Meeting 2017, 2017.
BibTeX | Tags: BCI, dynamical systems, movement model, neuroprosthetic
@conference{nokey,
title = {Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
author = {Klaes C I. Iossifidis},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {SfN Meeting 2017},
keywords = {BCI, dynamical systems, movement model, neuroprosthetic},
pubstate = {published},
tppubtype = {conference}
}
28.
Ioannis Iossifidis; Muhammad Ayaz Hussain; Christian Klaes
Temporal stabilized arm movement for efficient neuroprosthetic control by individuals with tetraplegia Miscellaneous
2017.
Abstract | BibTeX | Tags: dynamical systems, movement model, neuroprosthetic, Robotics
@misc{Iossifidis2017ab,
title = {Temporal stabilized arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
author = {Ioannis Iossifidis and Muhammad Ayaz Hussain and Christian Klaes},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
publisher = {SfN 2017},
abstract = {The generation of discrete movement with distinct and stable time courses characterizes each human movement and reflect the need to perform catching and interception tasks and for timed action sequences, incorporating dynamically changing environmental constraints. Several lines of evidence suggest neuronal mechanism for the initiation of movements i.e. in the supplementary motor area (SMA) and the premotor cortex and for movement planning mechanism generating velocity profiles satisfying time constraints. In order to meet the requirements of on-line evolving trajectories we propose a model, based on dynamical systems which describes goal directed trajectories in humans and generates trajectories for redundant anthropomorphic robotic arms The current study aim to evaluate the temporal characteristics of primary motor and posterior parietal cortex in patients with tetraplegia by using inception task implemented in virtual reality. The participants will be implanted with two 96-channel intracortical microelectrode arrays in the Primary Motor and Post Parietal Cortex. In the training phase the participants will be confronted with the observation of a robotic arm intercepting the bob of a pendulum at the lowest point of it's trajectory (maximum velocity) - the end effector reaches at the same time as the bob of the pendulum the lowest point of the trajectory performing a perfectly timed movement. The arm is positioned perpendicular to the oscillation plane exactly at the hight of the interception point to generate a one dimensional trajectory to the target. The time to contact between the robot's end effector and the bob of the pendulum is maintained constant and during the different sessions the distance between end effector and the point of interception is gradually increased. In order to catch up and to reach in time, either velocity formation or initiation time of the movement have to be changed. Both effects will be investigated independently. For the decoding of movement-related information we introduce a framework exploiting a deep learning approach with a convolutional neural networks.},
keywords = {dynamical systems, movement model, neuroprosthetic, Robotics},
pubstate = {published},
tppubtype = {misc}
}
The generation of discrete movement with distinct and stable time courses characterizes each human movement and reflect the need to perform catching and interception tasks and for timed action sequences, incorporating dynamically changing environmental constraints. Several lines of evidence suggest neuronal mechanism for the initiation of movements i.e. in the supplementary motor area (SMA) and the premotor cortex and for movement planning mechanism generating velocity profiles satisfying time constraints. In order to meet the requirements of on-line evolving trajectories we propose a model, based on dynamical systems which describes goal directed trajectories in humans and generates trajectories for redundant anthropomorphic robotic arms The current study aim to evaluate the temporal characteristics of primary motor and posterior parietal cortex in patients with tetraplegia by using inception task implemented in virtual reality. The participants will be implanted with two 96-channel intracortical microelectrode arrays in the Primary Motor and Post Parietal Cortex. In the training phase the participants will be confronted with the observation of a robotic arm intercepting the bob of a pendulum at the lowest point of it's trajectory (maximum velocity) - the end effector reaches at the same time as the bob of the pendulum the lowest point of the trajectory performing a perfectly timed movement. The arm is positioned perpendicular to the oscillation plane exactly at the hight of the interception point to generate a one dimensional trajectory to the target. The time to contact between the robot's end effector and the bob of the pendulum is maintained constant and during the different sessions the distance between end effector and the point of interception is gradually increased. In order to catch up and to reach in time, either velocity formation or initiation time of the movement have to be changed. Both effects will be investigated independently. For the decoding of movement-related information we introduce a framework exploiting a deep learning approach with a convolutional neural networks.
27.
Ioannis Iossifidis; Christian Klaes
Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia Miscellaneous
2017.
Abstract | BibTeX | Tags: BCI, dynamical systems, movement model, neuroprosthetic, Robotics
@misc{Iossifidis2017bb,
title = {Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
author = {Ioannis Iossifidis and Christian Klaes},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
publisher = {SfN 2017},
abstract = {Over the last decades the generation mechanism and the representation of goal- directed movements has been a topic of intensive neurophysiological research. The investigation in the motor, premotor, and parietal areas led to the discovery that the direction of hand's movement in space was encoded by populations of neurons in these areas together with many other movement parameters. These distributions of population activation reflect how movements are prepared ahead of movement initiation, as revealed by activity induced by cues that precede the imperative signal (Georgopoulos, 1991). Inspired by those findings a model based on dynamical systems was proposed both, to model goal directed trajectories in humans and to generate trajectories for redundant anthropomorphic robotic arms. The analysis of the attractor dynamics based on the qualitative comparison with measurements of resulting trajectories taken from arm movement experiments with humans (Grimme u. a., 2012) created a framework able to reproduce and to generate naturalistic human like arm trajectories (Iossifidis und Rano, 2013; Iossifidis, Schöner u. a., 2006). The main idea of the methodology is to choose low-dimensional, behavioral va- riables of the goal task can be represented as attractor states of those variables. The movement is generated through a dynamical system with attractors and repellers on the behavioral space, at the goal and constraint positions respectively. When the motion of the robot evolves according to the dynamics of these systems, the behavioral variables will be stabilized at their attractors. Movement is represented by the polar coordinates $phi$,$theta$ of the movement direction (heading direction) and the angular frequency $ømega$ of a hopf oscillator, generating the velocity profile of the arm movement. Therefore, the system dynamics will be expressed in terms of these variables. The target and each obstacle induce vector fields over these variables in a way that states where the hand is moving closer to the target are attractive, while states where it is moving towards an obstacle are repellant. Contributions from different sources are weighted by different factors, e.g. in the vicinity of an obstacle, the contribution from that obstacle must dominate the behavior to guarantee constraint satisfaction (collision prevention). Based on three parameters the presented framework is able to generate temporal stabilized (timed) discrete movements, dealing with disturbances and maintaining an approximately constant movement time. In the current study we will implant two 96-channel intracortical microelectrode arrays in the primary motor and the posterior parietal cortex (PPC) of an individual with tetraplegia. In the training phase the parameters of the dynamical systems will be tuned and optimized by machine learning algorithms. Rather controlling directly the arm movement and adjusting continuously parameters, the patient adjust by his or hers thoughts the three parameters of the dynamics, which remain almost constant during the movement. Only when the motion plan is changing the parameters have to be readjusted. The target directed trajectory evolves from the attractor solution of the dynamical systems equations, which means that the trajectory is generated while the system is in a stable stationary state, a fixed-point attractor. The increase of the degree of assistance lowers the cognitive load of the patient and enables the acknowledgement of the desired task without frustration. In addition we aim to replace the robotic manipulator by an exoskeleton for the upper body which will enable the patients to move his or hers own limbs, which would complete the development of a real neuroprosthetic device for every day use.},
keywords = {BCI, dynamical systems, movement model, neuroprosthetic, Robotics},
pubstate = {published},
tppubtype = {misc}
}
Over the last decades the generation mechanism and the representation of goal- directed movements has been a topic of intensive neurophysiological research. The investigation in the motor, premotor, and parietal areas led to the discovery that the direction of hand's movement in space was encoded by populations of neurons in these areas together with many other movement parameters. These distributions of population activation reflect how movements are prepared ahead of movement initiation, as revealed by activity induced by cues that precede the imperative signal (Georgopoulos, 1991). Inspired by those findings a model based on dynamical systems was proposed both, to model goal directed trajectories in humans and to generate trajectories for redundant anthropomorphic robotic arms. The analysis of the attractor dynamics based on the qualitative comparison with measurements of resulting trajectories taken from arm movement experiments with humans (Grimme u. a., 2012) created a framework able to reproduce and to generate naturalistic human like arm trajectories (Iossifidis und Rano, 2013; Iossifidis, Schöner u. a., 2006). The main idea of the methodology is to choose low-dimensional, behavioral va- riables of the goal task can be represented as attractor states of those variables. The movement is generated through a dynamical system with attractors and repellers on the behavioral space, at the goal and constraint positions respectively. When the motion of the robot evolves according to the dynamics of these systems, the behavioral variables will be stabilized at their attractors. Movement is represented by the polar coordinates $phi$,$theta$ of the movement direction (heading direction) and the angular frequency $ømega$ of a hopf oscillator, generating the velocity profile of the arm movement. Therefore, the system dynamics will be expressed in terms of these variables. The target and each obstacle induce vector fields over these variables in a way that states where the hand is moving closer to the target are attractive, while states where it is moving towards an obstacle are repellant. Contributions from different sources are weighted by different factors, e.g. in the vicinity of an obstacle, the contribution from that obstacle must dominate the behavior to guarantee constraint satisfaction (collision prevention). Based on three parameters the presented framework is able to generate temporal stabilized (timed) discrete movements, dealing with disturbances and maintaining an approximately constant movement time. In the current study we will implant two 96-channel intracortical microelectrode arrays in the primary motor and the posterior parietal cortex (PPC) of an individual with tetraplegia. In the training phase the parameters of the dynamical systems will be tuned and optimized by machine learning algorithms. Rather controlling directly the arm movement and adjusting continuously parameters, the patient adjust by his or hers thoughts the three parameters of the dynamics, which remain almost constant during the movement. Only when the motion plan is changing the parameters have to be readjusted. The target directed trajectory evolves from the attractor solution of the dynamical systems equations, which means that the trajectory is generated while the system is in a stable stationary state, a fixed-point attractor. The increase of the degree of assistance lowers the cognitive load of the patient and enables the acknowledgement of the desired task without frustration. In addition we aim to replace the robotic manipulator by an exoskeleton for the upper body which will enable the patients to move his or hers own limbs, which would complete the development of a real neuroprosthetic device for every day use.
26.
Christian Klaes; Ioannis Iossifidis
Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia Conference
SfN Meeting 2017, 2017.
BibTeX | Tags: BCI, dynamical systems, movement model, neuroprosthetic, Robotics
@conference{nokeyb,
title = {Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
author = {Christian Klaes and Ioannis Iossifidis},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {SfN Meeting 2017},
keywords = {BCI, dynamical systems, movement model, neuroprosthetic, Robotics},
pubstate = {published},
tppubtype = {conference}
}
2013
25.
Inaki Rano; Ioannis Iossifidis
Modelling human arm motion through the attractor dynamics approach Inproceedings
In: 2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013, pp. 2088–2093, 2013, ISBN: 9781479927449.
Abstract | Links | BibTeX | Tags: dynamical systems, movement model
@inproceedings{Rano2013,
title = {Modelling human arm motion through the attractor dynamics approach},
author = {Inaki Rano and Ioannis Iossifidis},
doi = {10.1109/ROBIO.2013.6739777},
isbn = {9781479927449},
year = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013},
pages = {2088--2093},
abstract = {Movement generation in robotics is an old problem with many excellent solutions. Most of them, however, look for optimality according to some metrics, but have no biological inspiration or cannot be used to imitate biological motion. For a human these techniques behave in a non-naturalistic way. This poses a problem for instance in human-robot interaction and, in general, for a good acceptance of robots in society. The present work presents a new analysis of the attractor dynamics approach to movement generation used in an anthropomorphic robot arm. Our analysis points to the possibility of using this approach to generate human-like arm trajectories in robots. One key property of human trajectories in pick-and-place tasks is the planarity of the trajectory of the end effector in 3D space. We show that this feature is also displayed by the attractor dynamic approach and, therefore, is a good candidate to the generation of naturalistic arm movements. textcopyright 2013 IEEE.},
keywords = {dynamical systems, movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
Movement generation in robotics is an old problem with many excellent solutions. Most of them, however, look for optimality according to some metrics, but have no biological inspiration or cannot be used to imitate biological motion. For a human these techniques behave in a non-naturalistic way. This poses a problem for instance in human-robot interaction and, in general, for a good acceptance of robots in society. The present work presents a new analysis of the attractor dynamics approach to movement generation used in an anthropomorphic robot arm. Our analysis points to the possibility of using this approach to generate human-like arm trajectories in robots. One key property of human trajectories in pick-and-place tasks is the planarity of the trajectory of the end effector in 3D space. We show that this feature is also displayed by the attractor dynamic approach and, therefore, is a good candidate to the generation of naturalistic arm movements. textcopyright 2013 IEEE.
24.
Ioannis Iossifidis; Ianki Rano
Modeling Human Arm Motion by Means of Attractor Dynamics Approach Inproceedings
In: Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2013), 2013.
Abstract | BibTeX | Tags: dynamical systems, movement model
@inproceedings{Iossifidis2013a,
title = {Modeling Human Arm Motion by Means of Attractor Dynamics Approach},
author = {Ioannis Iossifidis and Ianki Rano},
year = {2013},
date = {2013-01-01},
booktitle = {Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2013)},
abstract = {Autonomous robots with limited computational capacity call for control approaches that generate meaningful, goal-directed behavior without using a large amount of resources. The attractor dynamics approach to movement generation is a framework that links sensor data to motor commands via coupled dynamical systems that have attractors at behaviorally desired states. The low computational demands leave enough system resources for higher level function like forming a sequence of local goals to reach a distant one. The comparatively high performance of local behavior generation allows the global planning to be relatively simple.
In the present paper, we apply this approach to generate walking trajectories for a small humanoid robot, the Aldebaran Nao, that are goal-directed and avoid obstacles. The sensor information is a single camera in the head of the robot. The limited field of vision is compensated by head movements. The design of the dynamical system for motion generation and the choice of state variable makes a computationally expensive scene representation or local map building unnecessary.},
keywords = {dynamical systems, movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
Autonomous robots with limited computational capacity call for control approaches that generate meaningful, goal-directed behavior without using a large amount of resources. The attractor dynamics approach to movement generation is a framework that links sensor data to motor commands via coupled dynamical systems that have attractors at behaviorally desired states. The low computational demands leave enough system resources for higher level function like forming a sequence of local goals to reach a distant one. The comparatively high performance of local behavior generation allows the global planning to be relatively simple.
In the present paper, we apply this approach to generate walking trajectories for a small humanoid robot, the Aldebaran Nao, that are goal-directed and avoid obstacles. The sensor information is a single camera in the head of the robot. The limited field of vision is compensated by head movements. The design of the dynamical system for motion generation and the choice of state variable makes a computationally expensive scene representation or local map building unnecessary.
In the present paper, we apply this approach to generate walking trajectories for a small humanoid robot, the Aldebaran Nao, that are goal-directed and avoid obstacles. The sensor information is a single camera in the head of the robot. The limited field of vision is compensated by head movements. The design of the dynamical system for motion generation and the choice of state variable makes a computationally expensive scene representation or local map building unnecessary.
23.
Inaki Rano; Ioannis Iossifidis
Modelling human arm motion through the attractor dynamics approach Inproceedings
In: 2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013, pp. 2088–2093, 2013, ISBN: 9781479927449.
Abstract | Links | BibTeX | Tags: arm movement model, dynamical systems, movement model, Robotics
@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}
}
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.
22.
Ioannis Iossifidis; Ianki Rano
Modeling Human Arm Motion by Means of Attractor Dynamics Approach Inproceedings
In: Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2013), 2013.
Abstract | BibTeX | Tags: arm movement model, dynamical systems, movement model, Robotics
@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}
}
Autonomous robots with limited computational capacity call for control approaches that generate meaningful, goal-directed behavior without using a large amount of resources. The attractor dynamics approach to movement generation is a framework that links sensor data to motor commands via coupled dynamical systems that have attractors at behaviorally desired states. The low computational demands leave enough system resources for higher level function like forming a sequence of local goals to reach a distant one. The comparatively high performance of local behavior generation allows the global planning to be relatively simple.
In the present paper, we apply this approach to generate walking trajectories for a small humanoid robot, the Aldebaran Nao, that are goal-directed and avoid obstacles. The sensor information is a single camera in the head of the robot. The limited field of vision is compensated by head movements. The design of the dynamical system for motion generation and the choice of state variable makes a computationally expensive scene representation or local map building unnecessary.
In the present paper, we apply this approach to generate walking trajectories for a small humanoid robot, the Aldebaran Nao, that are goal-directed and avoid obstacles. The sensor information is a single camera in the head of the robot. The limited field of vision is compensated by head movements. The design of the dynamical system for motion generation and the choice of state variable makes a computationally expensive scene representation or local map building unnecessary.
2010
21.
M A Grimm; I Iossifidis
Behavioral organization for mobile robotic systems: An attractor dynamics approach Inproceedings
In: Joint 41st International Symposium on Robotics and 6th German Conference on Robotics 2010, ISR/ROBOTIK 2010, 2010, ISBN: 9781617387197.
Abstract | BibTeX | Tags: behavior generation, dynamical systems, movement model, Robotics
@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}
}
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.
20.
Hendrik Reimann; Ioannis Iossifidis; Gregor Schöner
End-effector obstacle avoidance using multiple dynamic variables Inproceedings
In: ISR / ROBOTIK 2010, Munich, Germany, 2010.
Abstract | BibTeX | Tags: behavior generation, dynamical systems, movement model, Robotics
@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}
}
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.
19.
Matthias Grimm; Ioannis Iossifidis
Behavioral Organization for Mobile Robotic Systems: An Attractor Dynamics Approach Inproceedings
In: ISR / ROBOTIK 2010, Munich, Germany, 2010.
Abstract | BibTeX | Tags: behavior generation, dynamical systems, movement model, movile robot, Robotics
@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}
}
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.
18.
Yulia Sandamirskaya; John Lipinski; Ioannis Iossifidis; G Schöner
Natural human-robot interaction through spatial language: a dynamic neural fields approach Inproceedings
In: Proc. 19th IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2010), pp. 600–607, IEEE, 2010, ISSN: 1944-9445.
Links | BibTeX | Tags: behavior generation, dynamical systems, man machine interaction, movement model, Robotics, 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 = {behavior generation, dynamical systems, man machine interaction, movement model, Robotics, speech recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
17.
Hendrik Reimann; Ioannis Iossifidis; Gregor Schöner
End-effector obstacle avoidance using multiple dynamic variables Inproceedings
In: ISR / ROBOTIK 2010, Munich, Germany, 2010.
Abstract | BibTeX | Tags: arm movement model, behavior generation, dynamical systems, movement model, obstacle avoidance, Robotics
@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}
}
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.
16.
Matthias Grimm; Ioannis Iossifidis
Behavioral Organization for Mobile Robotic Systems: An Attractor Dynamics Approach Inproceedings
In: ISR / ROBOTIK 2010, Munich, Germany, 2010.
Abstract | BibTeX | Tags: behavior generation, dynamical systems, movement model, movile robot, Robotics
@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}
}
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.
15.
Yulia Sandamirskaya; John Lipinski; Ioannis Iossifidis; G Schöner
Natural human-robot interaction through spatial language: a dynamic neural fields approach Inproceedings
In: Proc. 19th IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2010), pp. 600–607, IEEE, 2010, ISSN: 1944-9445.
Links | BibTeX | Tags: arm movement model, behavior generation, dynamical systems, man machine interaction, movement model, Robotics, speech recognition
@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)},
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pubstate = {published},
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2005
14.
I Iossifidis; A Steinhage
Behavior generation for Anthropomorphic robots by means of dynamical systems Book
2005, ISSN: 16107438.
Abstract | BibTeX | Tags: behavior generation, dynamical systems, movement model, Robotics
@book{Iossifidis2005c,
title = {Behavior generation for Anthropomorphic robots by means of dynamical systems},
author = {I Iossifidis and A Steinhage},
issn = {16107438},
year = {2005},
date = {2005-01-01},
booktitle = {Springer Tracts in Advanced Robotics},
volume = {14},
abstract = {This article describes the current state of our research on anthropomorphic robots. Our aim is to make the reader familiar with the two basic principles our work is based on: anthropomorphism and dynamics. The principle of anthropomorphism means a restriction to human-like robots which use version, audition and touch as their only sensors so that natural man-machine interaction is possible. The principle of dynamics stands for the mathematical framework based on which our robots generate their behavior. Both principles have their root in the idea that concepts of biological behavior and information processing can be exploited to control technical systems. textcopyright Springer-Verlag Berlin Heidelberg 2005.},
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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.
13.
Ioannis Iossifidis; A Steinhage
Behavior generation for Anthropomorphic robots by means of dynamical systems Book
2005, ISSN: 16107438.
Abstract | BibTeX | Tags: arm movement model, behavior generation, dynamical systems, movement model, Robotics
@book{Iossifidis2005cb,
title = {Behavior generation for Anthropomorphic robots by means of dynamical systems},
author = {Ioannis Iossifidis and A Steinhage},
issn = {16107438},
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urldate = {2005-01-01},
booktitle = {Springer Tracts in Advanced Robotics},
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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.},
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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
12.
Erwin Prassler; Gisbert Lawitzky; Andreas Stopp; Gerhard Grunwald; Martin Ħägele; Rüdiger Đillmann; Ioannis Iossifidis
Advances in Ħuman Robot Interaction Book
Springer Press, 2004.
Abstract | Links | BibTeX | Tags: behavior generation, dynamical systems, movement model, Robotics
@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},
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Motion Coordination
Multi-Modal Robot Interfaces
Physical Interaction between Humans and Robots
Robot Learning
Visual Instruction of Robots},
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Human Robot Interaction and Cooperation
Motion Coordination
Multi-Modal Robot Interfaces
Physical Interaction between Humans and Robots
Robot Learning
Visual Instruction of Robots
Motion Coordination
Multi-Modal Robot Interfaces
Physical Interaction between Humans and Robots
Robot Learning
Visual Instruction of Robots
11.
Ioannis Iossifidis; Gregor Schöner
Attractor dynamics approach for autonomous collision-free path generation in 3d-space for an 7 dof robot arm Inproceedings
In: Proceedings of the ROBOTIK 2004, Leistungsstand - Anwendungen - Visionen - Trends, number 1841 in VDI-Berichte, pp. 815–822, VDI/VDE VDI Verlag, München, Germany, 2004.
BibTeX | Tags: collision avoidance, dynamical systems, Inverse kinematics, movement model, Robotics
@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 = {collision avoidance, dynamical systems, Inverse kinematics, movement model, Robotics},
pubstate = {published},
tppubtype = {inproceedings}
}
10.
Ioannis Iossifidis; Carsten Bruckhoff; Christoph Theis; Claudia Grote; Christian Faubel; Gregor Schöner
A Cooperative Robot Assistant CoRA For Human Environments Incollection
In: Prassler, Erwin; Lawitzky, Gisbert; Stopp, Andreas; Grunwald, Gerhard; Hägele, Martin; Dillmann, Rüdiger; Iossifidis, Ioannis (Ed.): Advances in Human Robot Interaction, vol. 14/2004, no. ISBN: 3-540-23211-7,, pp. 385–401, Springer Press, 2004, ISBN: 3-540-23211-7,.
Abstract | Links | BibTeX | Tags: behavior generation, dynamical systems, movement model, Robotics
@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},
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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.
9.
Ioannis Iossifidis; Axel Steinhage
Behavior Generation For Anthropomorphic Robots by Means of Dynamical Systems Incollection
In: Prassler, Erwin; Lawitzky, Gisbert; Stopp, Andreas; Grunwald, Gerhard; Hägele, Martin; Dillmann, Rüdiger; Iossifidis, Ioannis (Ed.): Advances in Human Robot Interaction, vol. 14/2004, no. ISBN: 3-540-23211-7,, pp. 269–300, Springer Press, 2004, ISBN: 3-540-23211-7,.
Abstract | Links | BibTeX | Tags: behavior generation, dynamical systems, movement model, Robotics
@incollection{Iossifidis2004e,
title = {Behavior Generation For Anthropomorphic Robots by Means of Dynamical Systems},
author = {Ioannis Iossifidis and Axel Steinhage},
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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.
8.
Erwin Prassler; Gisbert Lawitzky; Andreas Stopp; Gerhard Grunwald; Martin Hägele; Rüdiger Dillmann; Ioannis Iossifidis
Advances in Human Robot Interaction Book
Springer Press, 2004.
Abstract | Links | BibTeX | Tags: behavior generation, dynamical systems, Inverse kinematics, movement model, redundant robot arm, Robotics
@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},
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Human Robot Interaction and Cooperation Motion Coordination Multi-Modal Robot Interfaces Physical Interaction between Humans and Robots Robot Learning Visual Instruction of Robots
7.
Erwin Prassler; Gisbert Lawitzky; Andreas Stopp; Gerhard Grunwald; Martin Hägele; Rüdiger Dillmann; Ioannis Iossifidis
Advances in Human Robot Interaction (Springer Tracts in Advanced Robotics) Book
Springer, 2004, ISBN: 3540232117.
Links | BibTeX | Tags: behavior generation, dynamical systems, Inverse kinematics, movement model, redundant robot arm, Robotics
@book{Prassler2004c,
title = {Advances in Human Robot Interaction (Springer Tracts in Advanced Robotics)},
author = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Hägele and Rüdiger Dillmann and Ioannis Iossifidis},
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6.
Erwin Prassler; Gisbert Lawitzky; Andreas Stopp; Gerhard Grunwald; Martin Ħägele; Rüdiger Đillmann; Ioannis Iossifidis
Advances in Ħuman Robot Interaction Book
Springer Press, 2004.
Abstract | Links | BibTeX | Tags: arm movement model, behavior generation, dynamical systems, movement model, Robotics
@book{Prassler2004d,
title = {Advances in Ħuman Robot Interaction},
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Motion Coordination
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Physical Interaction between Humans and Robots
Robot Learning
Visual Instruction of Robots},
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Human Robot Interaction and Cooperation
Motion Coordination
Multi-Modal Robot Interfaces
Physical Interaction between Humans and Robots
Robot Learning
Visual Instruction of Robots
Motion Coordination
Multi-Modal Robot Interfaces
Physical Interaction between Humans and Robots
Robot Learning
Visual Instruction of Robots
5.
Ioannis Iossifidis; Gregor Schöner
Attractor dynamics approach for autonomous collision-free path generation in 3d-space for an 7 dof robot arm Inproceedings
In: Proceedings of the ROBOTIK 2004, Leistungsstand - Anwendungen - Visionen - Trends, number 1841 in VDI-Berichte, pp. 815–822, VDI/VDE VDI Verlag, München, Germany, 2004.
BibTeX | Tags: arm movement model, collision avoidance, dynamical systems, Inverse kinematics, movement model, Robotics
@inproceedings{Iossifidis2004ab,
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},
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pubstate = {published},
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4.
Ioannis Iossifidis; Carsten Bruckhoff; Christoph Theis; Claudia Grote; Christian Faubel; Gregor Schöner
A Cooperative Robot Assistant CoRA For Human Environments Incollection
In: Prassler, Erwin; Lawitzky, Gisbert; Stopp, Andreas; Grunwald, Gerhard; Hägele, Martin; Dillmann, Rüdiger; Iossifidis, Ioannis (Ed.): Advances in Human Robot Interaction, vol. 14/2004, no. ISBN: 3-540-23211-7,, pp. 385–401, Springer Press, 2004, ISBN: 3-540-23211-7,.
Abstract | Links | BibTeX | Tags: arm movement model, behavior generation, dynamical systems, movement model, Robotics
@incollection{Iossifidis2004db,
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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},
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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.
Ioannis Iossifidis; Axel Steinhage
Behavior Generation For Anthropomorphic Robots by Means of Dynamical Systems Incollection
In: Prassler, Erwin; Lawitzky, Gisbert; Stopp, Andreas; Grunwald, Gerhard; Hägele, Martin; Dillmann, Rüdiger; Iossifidis, Ioannis (Ed.): Advances in Human Robot Interaction, vol. 14/2004, no. ISBN: 3-540-23211-7,, pp. 269–300, Springer Press, 2004, ISBN: 3-540-23211-7,.
Abstract | Links | BibTeX | Tags: arm movement model, behavior generation, dynamical systems, movement model, Robotics
@incollection{Iossifidis2004eb,
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author = {Ioannis Iossifidis and Axel Steinhage},
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2.
Erwin Prassler; Gisbert Lawitzky; Andreas Stopp; Gerhard Grunwald; Martin Hägele; Rüdiger Dillmann; Ioannis Iossifidis
Advances in Human Robot Interaction Book
Springer Press, 2004.
Abstract | Links | BibTeX | Tags: arm movement model, behavior generation, dynamical systems, Inverse kinematics, movement model, redundant robot arm, Robotics
@book{Prassler2004bb,
title = {Advances in Human Robot Interaction},
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Human Robot Interaction and Cooperation Motion Coordination Multi-Modal Robot Interfaces Physical Interaction between Humans and Robots Robot Learning Visual Instruction of Robots
1.
Erwin Prassler; Gisbert Lawitzky; Andreas Stopp; Gerhard Grunwald; Martin Hägele; Rüdiger Dillmann; Ioannis Iossifidis
Advances in Human Robot Interaction (Springer Tracts in Advanced Robotics) Book
Springer, 2004, ISBN: 3540232117.
Links | BibTeX | Tags: arm movement model, behavior generation, dynamical systems, Inverse kinematics, movement model, redundant robot arm, Robotics
@book{Prassler2004cb,
title = {Advances in Human Robot Interaction (Springer Tracts in Advanced Robotics)},
author = {Erwin Prassler and Gisbert Lawitzky and Andreas Stopp and Gerhard Grunwald and Martin Hägele and Rüdiger Dillmann and Ioannis Iossifidis},
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