Beyond Top-Grasps Through Scene Completion

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Abstract

Current end-to-end grasp planning methods propose grasps in the order of (milli)seconds that attain high grasp success rates on a diverse set of objects, but often by constraining the workspace to top-grasps. In this work, we present a method that allows end-to-end top grasp planning methods to generate full six-degree-of-freedom grasps using a single RGB-D view as input. This is achieved by estimating the complete shape of the object to be grasped, then simulating different viewpoints of the object, passing the simulated viewpoints to an end-to-end grasp generation method, and finally executing the overall best grasp. The method was experimentally validated on a Franka Emika Panda by comparing 429 grasps generated by the state-of-the-art Fully Convolutional Grasp Quality CNN, both on simulated and real camera viewpoints. The results show statistically significant improvements in terms of grasp success rate when using simulated viewpoints over real camera viewpoints, especially when the real camera viewpoint is angled.
Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Robotics and Automation, ICRA 2020
PublisherIEEE
Pages545-551
Number of pages7
ISBN (Electronic)978-1-7281-7395-5
DOIs
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Robotics and Automation - Online
Duration: 31 May 202031 Aug 2020

Publication series

NameIEEE International Conference on Robotics and Automation
PublisherIEEE
ISSN (Print)2152-4092
ISSN (Electronic)2379-9552

Conference

ConferenceIEEE International Conference on Robotics and Automation
Abbreviated titleICRA
Period31/05/202031/08/2020

Keywords

  • Shape
  • Cameras
  • Grasping
  • Planning
  • Robot vision systems
  • Pipelines

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