A lot of information which is used in our reach or grasp models is visual. Or, can be extracted from visual information (like distance, position, size, orientation, etc.). In the last weeks, we add image processing module built upon OpenCV library to our project and we use several OpenCV functions to process iCub’s camera image [...]
In our work, we study how methods of machine learning could be used to let iCub learn to reach and grasp objects. Simulator offers various propriocentric information about its state. Hovewer, we could get only the position of the center of iCub’s hand and that is not enough to calculate hand orientation in the space [...]
Today I implemented simple algorithm to lift object when iCub successfully grasps it. While iCub was learning (online learning of Grasping CACLA), I took this very nice screenshot of sucessful grasp and lift of the grasped object. Video – iCub learns grasping (after 200-300 learning epizodes)
In the last two months I did several experiments on my iCub simulator. Reaching I had enhanced my reaching module and tested it with various parameters and state/action representations. In each experiment I train reaching CACLA for 1000-2000 episodes and test it on the fly after every 50 episodes. In most of experiments I used [...]
I found useful pictures of hand movement terminology. These terms are essential to understand iCub’s hand and fingers DoF. I have read about FARS model of control of reaching and grasping (Fagg, Arbib et al.) Another article which copes with grasping models is Models for the control of grasping (Oztop and Kawato). Then I’ve drawn [...]
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Master thesis of Lukáš Zdechovan,
a master degree student of Applied Informatics at Comenius University. Thesis is lead by docent Igor Farkaš.Categories
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