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Robust and fast generation of top and side grasps for unknown objects

Brice Denoun, Beatriz León, Claudio Zito, Rustam Stolkin, Lorenzo Jamone, Miles Hansard

Year
2019
Citations
2
Access
Open access

Abstract

In this work, we present a geometry-based grasping algorithm that is capable of efficiently generating both top and side grasps for unknown objects, using a single view RGB-D camera, and of selecting the most promising one. We demonstrate the effectiveness of our approach on a picking scenario on a real robot platform. Our approach has shown to be more reliable than another recent geometry-based method considered as baseline [7] in terms of grasp stability, by increasing the successful grasp attempts by a factor of six.

Keywords

GRASPComputer scienceComputer visionArtificial intelligenceBaseline (sea)RGB color modelRobotStability (learning theory)RoboticsMachine learning

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