Learning to Generate 6-DoF Grasp Poses with Reachability Awareness

Xibai Lou, Yang Yang, Changhyun Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Motivated by the stringent requirements of unstructured real-world where a plethora of unknown objects reside in arbitrary locations of the surface, we propose a voxel-based deep 3D Convolutional Neural Network (3D CNN) that generates feasible 6-DoF grasp poses in unrestricted workspace with reachability awareness. Unlike the majority of works that predict if a proposed grasp pose within the restricted workspace will be successful solely based on grasp pose stability, our approach further learns a reachability predictor that evaluates if the grasp pose is reachable or not from robot's own experience. To avoid the laborious real training data collection, we exploit the power of simulation to train our networks on a large-scale synthetic dataset. This work is an early attempt that simultaneously learns grasping reachability while proposing feasible grasp poses with 3D CNN. Experimental results in both simulation and real-world demonstrate that our approach outperforms several other methods and achieves 82.5% grasping success rate on unknown objects.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1532-1538
Number of pages7
ISBN (Electronic)9781728173955
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
Duration: May 31 2020Aug 31 2020

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020
CountryFrance
CityParis
Period5/31/208/31/20

Bibliographical note

Funding Information:
*This work was in part supported by the MnDRIVE Initiative on Robotics, Sensors, and Advanced Manufacturing. 1X. Lou and C. Choi are with the Department of Electrical and Computer Engineering, Univ. of Minnesota, Minneapolis, USA {lou00015, cchoi}@umn.edu 2Y. Yang is with the Department of Computer Science and Engineering, Univ. of Minnesota, Minneapolis, USA yang5276@umn.edu

Keywords

  • Deep Learning in Robotics and Automation
  • Grasping
  • Perception for Grasping and Manipulation

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