Jochen J. Steil
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Jochen J. Steil has a total of 17 co-authors. Here are the top co-authors by number of shared publications:
H. Ritter
Neuroinformatics Group, Faculty of Technology, Bielefeld University, P.O. Box 100131, D-33501 Bielefeld, Germany
2 shared publications- Dmitry V. Lebedev
Neuroinformatics Group, Faculty of Technology, University of Bielefeld, P.O.-Box 10 01 31, D-33501 Bielefeld, Germany
1 shared publication E. Körner
Honda Research Institute GmbH, Carl-Legien-Str. 30, 63073 Offenbach, Germany
1 shared publicationF. Roethling
1 shared publication- Fabrice Rossi
SAMM EA 4534
1 shared publication G. A. Fink
Faculty of Technology Bielefeld University P.O.-Box 10 01 31 D-33501 Bielefeld Germany
1 shared publication
Latest Publications
- 1 April 2007
- Published by Elsevier in Neural Networks
- Vol. 20 (3) , 353-364
Online reservoir adaptation by intrinsic plasticity for backpropagation–decorrelation and echo state learning
- 1 April 2007
- Published by Elsevier in Neural Networks
- Vol. 20 (3) , 353-364
Online reservoir adaptation by intrinsic plasticity for backpropagation–decorrelation and echo state learning
- 1 March 2007
- Published by Elsevier in Neurocomputing
- Vol. 70 (7-9) , 1235-1246
Adaptive scene dependent filters for segmentation and online learning of visual objects
- 1 March 2007
- Published by Elsevier in Neurocomputing
- Vol. 70 (7-9) , 1235-1246
Adaptive scene dependent filters for segmentation and online learning of visual objects
- 1 October 2006
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 3879-3884
Dynamic Path Planning for a 7-DOF Robot Arm
We present an on-line, robust, and efficient path planner for the redundant Mitsubishi PA-10 arm with 7 degrees of freedom (DOF) in non-stationary environments. Because of the specific kinematic model of the arm, path planning can be first reduced to a redundant 6-DOF problem in a 5D configuration space, which can be further decomposed into two problems: (i) 3D position planning in Cartesian space and (ii) planning in a 3D space composed of two orientation angles and an explicit parameterization of the arms redundancy. Position and orientation planning are interweaving and performed "on-the-fly" without explicit global knowledge of the environment using two instances of the dynamic wave expansion neural network (DWENN), an effective method for path generation in arbitrarily changing environments. The dynamic and explorative nature of the DWENN algorithm allows to treat stationary and dynamic obstacles in a unified manner. Through a number of simulative tests, we show that the planner is capable of reaching both a satisfactory robustness level and real-time performance, as required by many practical applications
- 1 October 2006
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 3879-3884
Dynamic Path Planning for a 7-DOF Robot Arm
We present an on-line, robust, and efficient path planner for the redundant Mitsubishi PA-10 arm with 7 degrees of freedom (DOF) in non-stationary environments. Because of the specific kinematic model of the arm, path planning can be first reduced to a redundant 6-DOF problem in a 5D configuration space, which can be further decomposed into two problems: (i) 3D position planning in Cartesian space and (ii) planning in a 3D space composed of two orientation angles and an explicit parameterization of the arms redundancy. Position and orientation planning are interweaving and performed "on-the-fly" without explicit global knowledge of the environment using two instances of the dynamic wave expansion neural network (DWENN), an effective method for path generation in arbitrarily changing environments. The dynamic and explorative nature of the DWENN algorithm allows to treat stationary and dynamic obstacles in a unified manner. Through a number of simulative tests, we show that the planner is capable of reaching both a satisfactory robustness level and real-time performance, as required by many practical applications
- 31 March 2006
- Published by Elsevier in Neurocomputing
- Vol. 69 (7) , 625-626
New Issues in Neurocomputing
- 31 March 2006
- Published by Elsevier in Neurocomputing
- Vol. 69 (7) , 625-626
New Issues in Neurocomputing
- Open Access
Multi-Modal Human-Machine Communication for Instructing Robot Grasping Tasks
A major challenge for the realization of intelligent robots is to supply them with cognitive abilities in order to allow ordinary users to program them easily and intuitively. One way of such programming is teaching work tasks by interactive demonstration. To make this effective and convenient for the user, the machine must be capable to establish a common focus of attention and be able to use and integrate spoken instructions, visual perceptions, and non-verbal clues like gestural commands. We report progress in building a hybrid architecture that combines statistical methods, neural networks, and finite state machines into an integrated system for instructing grasping tasks by man-machine interaction. The system combines the GRAVIS-robot for visual attention and gestural instruction with an intelligent interface for speech recognition and linguistic interpretation, and an modality fusion module to allow multi-modal task-oriented man-machine communication with respect to dextrous robot manipulation of objects.
Open AccessMulti-Modal Human-Machine Communication for Instructing Robot Grasping Tasks
A major challenge for the realization of intelligent robots is to supply them with cognitive abilities in order to allow ordinary users to program them easily and intuitively. One way of such programming is teaching work tasks by interactive demonstration. To make this effective and convenient for the user, the machine must be capable to establish a common focus of attention and be able to use and integrate spoken instructions, visual perceptions, and non-verbal clues like gestural commands. We report progress in building a hybrid architecture that combines statistical methods, neural networks, and finite state machines into an integrated system for instructing grasping tasks by man-machine interaction. The system combines the GRAVIS-robot for visual attention and gestural instruction with an intelligent interface for speech recognition and linguistic interpretation, and an modality fusion module to allow multi-modal task-oriented man-machine communication with respect to dextrous robot manipulation of objects.
- 20 August 2004
- Published by Elsevier in Neurocomputing
- Vol. 63, 5-23
Analyzing the weight dynamics of recurrent learning algorithms
- 20 August 2004
- Published by Elsevier in Neurocomputing
- Vol. 63, 5-23
Analyzing the weight dynamics of recurrent learning algorithms
- 31 October 2002
- Published by Elsevier in Neurocomputing
- Vol. 48 (1-4) , 39-51
Local stability of recurrent networks with time-varying weights and inputs
- 31 October 2002
- Published by Elsevier in Neurocomputing
- Vol. 48 (1-4) , 39-51
Local stability of recurrent networks with time-varying weights and inputs