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arXiv · 1806.03486

Learning to Grasp from a Single Demonstration

Pieter Van Molle·Tim Verbelen·Elias De Coninck·Cedric De Boom·Pieter Simoens·Bart Dhoedt
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Abstract

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.

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Pieter Van Molle, Tim Verbelen, Elias De Coninck, Cedric De Boom, Pieter Simoens, Bart Dhoedt. 2018-06-09. Learning to Grasp from a Single Demonstration. https://arxiv.org/abs/1806.03486

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