Learning Keypoints from Synthetic Data for Robotic Cloth Folding

Robotic manipulation of deformable objects is a difficult activity. However, responsibilities like cloth manipulation are handy in daily options. A latest paper released on arXiv.org proposes a novel solution for the towel folding task.

Graphic credit score: Piqsels, CC0 General public Domain

Scientists advise employing a convolutional neural network (CNN) as a keypoint detector to estimate the 2D positions of the towel corners from a solitary RGB picture. Then, a scripted, open up-loop grasp and a quasi-static fold trajectory are executed based on these semantic keypoints. The keypoint detector is educated solely on artificial information.

It is revealed that artificial knowledge is suited for the detection of keypoints for cloth folding. The evaluation of the overall performance of the system reveals that the grasp achievements amount is about 77%, and the fold good results charge is about 53% therefore, a lot more in depth tuning is demanded to completely overcome

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