Machine learning of inductive bias
Machine learning of inductive bias
Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
CVGIP: Image Understanding
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Dynamic cell structure learns perfectly topology preserving map
Neural Computation
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Robot grasp synthesis algorithms: a survey
International Journal of Robotics Research
Active learning for vision-based robot grasping
Machine Learning - Special issue on robot learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Learning to Grasp Using Visual Information
Learning to Grasp Using Visual Information
Revised Papers from the International Workshop on Sensor Based Intelligent Robots
A strategy for grasping unknown objects based on co-planarity and colour information
Robotics and Autonomous Systems
Active learning of visual descriptors for grasping using non-parametric smoothed beta distributions
Robotics and Autonomous Systems
| Hi-index | 0.00 |
We apply techniques of computer vision and neural networklearning to get a versatile robot manipulator. All work conductedfollows the principle of autonomous learning from visualdemonstration. The user must demonstrate the relevant objects,situations, and/or actions, and the robot vision system must learnfrom those. For approaching and grasping technical objects threeprincipal tasks have to be done—calibrating the camera-robotcoordination, detecting the desired object in the images, andchoosing a stable grasping pose. These procedures are based on(nonlinear) functions, which are not known a priori and thereforehave to be learned. We uniformly approximate the necessary functionsby networks of gaussian basis functions (GBF networks). By modifyingthe number of basis functions and/or the size of the gaussian supportthe quality of the function approximation changes. The appropriateconfiguration is learned in the training phase and applied during theoperation phase. All experiments are carried out in real worldapplications using an industrial articulation robot manipulator andthe computer vision system KHOROS.