Beyond Top-Grasps Through Scene Completion

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu


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.
OtsikkoProceedings of the IEEE Conference on Robotics and Automation, ICRA 2020
JulkaisupaikkaParis, France
TilaHyväksytty/In press - 22 tammikuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Conference on Robotics and Automation - Online
Kesto: 31 toukokuuta 202031 elokuuta 2020


NimiIEEE International Conference on Robotics and Automation
ISSN (painettu)2152-4092
ISSN (elektroninen)2379-9552


ConferenceIEEE International Conference on Robotics and Automation

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  • Siteeraa tätä

    Lundell, J., Verdoja, F., & Kyrki, V. (Hyväksytty/painossa). Beyond Top-Grasps Through Scene Completion. teoksessa Proceedings of the IEEE Conference on Robotics and Automation, ICRA 2020 (IEEE International Conference on Robotics and Automation). IEEE.