Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robotic manipulator in the real or simulated world. We propose a simpler learning-from-demonstration approach that is able to detect the object to grasp from merely a single demonstration using a convolutional neural network we call GraspNet. In order to increase robustness and decrease the training time even further, we leverage data from previous demonstrations to quickly fine-tune a GrapNet for each new demonstration. We present some preliminary results on a grasping experiment with the Franka Panda cobot for which we can train a GraspNet with only hundreds of train iterations.
@article{arxiv.1806.03486,
title = {Learning to Grasp from a Single Demonstration},
author = {Pieter Van Molle and Tim Verbelen and Elias De Coninck and Cedric De Boom and Pieter Simoens and Bart Dhoedt},
journal= {arXiv preprint arXiv:1806.03486},
year = {2018}
}
Comments
10 pages, 5 figures, IAS-15 2018 workshop on Learning Applications for Intelligent Autonomous Robots