Machine vision: automated visual inspection and robot vision
Machine vision: automated visual inspection and robot vision
Visually guided movements: learning with modular neural maps in robotics
Neural Networks - Special issue on neural control and robotics: biology and technology
Visual Control of Robots: High-Performance Visual Serving
Visual Control of Robots: High-Performance Visual Serving
Computer Vision
Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
Applied Intelligence
Statistical Learning for Humanoid Robots
Autonomous Robots
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
AFPAC '00 Proceedings of the Second International Workshop on Algebraic Frames for the Perception-Action Cycle
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Channel Smoothing: Efficient Robust Smoothing of Low-Level Signal Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Neural Networks for Visual Servoing Using Evolutionary Methods
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Accurate interpolation in appearance-based pose estimation
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
A model of reaching that integrates reinforcement learning and population encoding of postures
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
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In this paper, we present a visual servoing method based on a learned mapping between feature space and control space. Using a suitable recognition algorithm, we present and evaluate a complete method that simultaneously learns the appearance and control of a low-cost robotic arm. The recognition part is trained using an action precedes perception approach. The novelty of this paper, apart from the visual servoing method per se, is the combination of visual servoing with gripper recognition. We show that we can achieve high precision positioning without knowing in advance what the robotic arm looks like or how it is controlled.