Grasp planning and most specifically the grasp space exploration is still an open issue in robotics. This article presents a data-driven oriented methodology to model the grasp space of a multi-fingered adaptive gripper for known objects. This method relies on a limited dataset of manually specified expert grasps, and uses variational autoencoder to learn grasp intrinsic features in a compact way from a computational point of view. The learnt model can then be used to generate new non-learnt gripper configurations to explore the grasp space.
@article{arxiv.2109.08504,
title = {Learning to Model the Grasp Space of an Underactuated Robot Gripper Using Variational Autoencoder},
author = {Clément Rolinat and Mathieu Grossard and Saifeddine Aloui and Christelle Godin},
journal= {arXiv preprint arXiv:2109.08504},
year = {2021}
}