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arXiv · 2109.09572

Human Initiated Grasp Space Exploration Algorithm for an Underactuated Robot Gripper Using Variational Autoencoder

Clément Rolinat·Mathieu Grossard·Saifeddine Aloui·Christelle Godin
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Abstract

Grasp planning and most specifically the grasp space exploration is still an open issue in robotics. This article presents an efficient procedure for exploring the grasp space of a multifingered adaptive gripper for generating reliable grasps given a known object pose. This procedure relies on a limited dataset of manually specified expert grasps, and use a mixed analytic and data-driven approach based on the use of a grasp quality metric and variational autoencoders. The performances of this method are assessed by generating grasps in simulation for three different objects. On this grasp planning task, this method reaches a grasp success rate of 99.91% on 7000 trials.

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Clément Rolinat, Mathieu Grossard, Saifeddine Aloui, Christelle Godin. 2021-09-20. Human Initiated Grasp Space Exploration Algorithm for an Underactuated Robot Gripper Using Variational Autoencoder. https://doi.org/10.1109/icra48506.2021.9561765

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