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Object Detection Approach for Robot Grasp Detection
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. Royal Inst Technol KTH, Sch Elect Engn & Comp Sci, SE-10044 Stockholm, Sweden..ORCID iD: 0000-0001-6671-9366
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-1170-7162
2019 (English)In: 2019 International Conference on Robotics And Automation (ICRA) / [ed] Howard, A Althoefer, K Arai, F Arrichiello, F Caputo, B Castellanos, J Hauser, K Isler, V Kim, J Liu, H Oh, P Santos, V Scaramuzza, D Ude, A Voyles, R Yamane, K Okamura, A, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 4953-4959, article id 8793751Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we focus on the robot grasping problem with parallel grippers using image data. For this task, we propose and implement an end-to-end approach. In order to detect the good grasping poses for a parallel gripper from RGB images, we have employed transfer learning for a Convolutional Neural Network (CNN) based object detection architecture. Our obtained results show that, the adapted network either outperforms or is on-par with the state-of-the art methods on a benchmark dataset. We also performed grasping experiments on a real robot platform to evaluate our method's real world performance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 4953-4959, article id 8793751
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-265429DOI: 10.1109/ICRA.2019.8793751ISI: 000494942303087Scopus ID: 2-s2.0-85071451407ISBN: 978-1-5386-6026-3 (print)OAI: oai:DiVA.org:kth-265429DiVA, id: diva2:1377546
Conference
2019 International Conference on Robotics and Automation, ICRA 2019; Palais des Congres de Montreal, Montreal; Canada; 20 May 2019 through 24 May 2019
Note

QC 20191212

Available from: 2019-12-12 Created: 2019-12-12 Last updated: 2019-12-12Bibliographically approved

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