Title: Learning to Grasp from a Single Demonstration,
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.