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A Cloud-based Network of 3D Objects for Robust Grasp Planning

Published: 04 January 2021 Publication History

Abstract

Recent developments in the field of robot grasping have shown great improvements in the grasp success rates when dealing with unknown objects. In this work we improve on one of the most promising approaches, the Grasp Quality Convolutional Neural Network (GQ-CNN) trained on the DexNet 2.0 dataset. We propose a new GG-CNN architecture for DexNet, provide a new way for dataset generation for the GG-CNN and describe practical improvements that increase the model validation accuracy and other performance aspects of the whole system

References

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David Fischinger. 2020. Robot Learning Lab: Personal Robotics, Co-Robots, Robotic Perception. Computer Science Department Cornell University. http://pr.cs.cornell.edu/grasping/rect_data/data.php.
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Sulabh Kumra and Christopher Kanan. 2017. Robotic grasp detection using deep convolutional neural networks. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 769–776.
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Sergey Levine, Peter Pastor, Alex Krizhevsky, Julian Ibarz, and Deirdre Quillen. 2018. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. The International Journal of Robotics Research 37, 4-5(2018), 421–436.
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Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, and Ken Goldberg. 2017. Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics. arXiv preprint arXiv:1703.09312(2017).
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Jeffrey Mahler, Florian T Pokorny, Brian Hou, Melrose Roderick, Michael Laskey, Mathieu Aubry, Kai Kohlhoff, Torsten Kröger, James Kuffner, and Ken Goldberg. 2016. Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards. In 2016 IEEE international conference on robotics and automation (ICRA). IEEE, 1957–1964.
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Vishal Satish, Jeffrey Mahler, and Ken Goldberg. 2019. On-policy dataset synthesis for learning robot grasping policies using fully convolutional deep networks. IEEE Robotics and Automation Letters 4, 2 (2019), 1357–1364.
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Cited By

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  • (2024)Enhancing Robotic Grasping of Free-Floating Targets with Soft Actor-Critic Algorithm and Tactile Sensors: a Focus on the Pre-Grasp StageAIAA SCITECH 2024 Forum10.2514/6.2024-2419Online publication date: 4-Jan-2024

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CCRIS '20: Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System
October 2020
217 pages
ISBN:9781450388054
DOI:10.1145/3437802
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 04 January 2021

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Author Tags

  1. Algorithm selection
  2. Dataset representation
  3. Dataset synthesis
  4. Generative adversarial nets
  5. Graph convolutional network
  6. Machine learning
  7. Meta-learning

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View all
  • (2024)Enhancing Robotic Grasping of Free-Floating Targets with Soft Actor-Critic Algorithm and Tactile Sensors: a Focus on the Pre-Grasp StageAIAA SCITECH 2024 Forum10.2514/6.2024-2419Online publication date: 4-Jan-2024

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