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References [1] A. Bicchi and V. Kumar. Robotic grasping and con- tact: Areview. In2000InternationalConferenceon Robotics and Automation (ICRA), volume 1, pages 348–353. IEEE,2000. [2] J.Bohg,A.Morales,T.Asfour,andD.Kragic. Data- driven grasp synthesisβ€”a survey. IEEE Transac- tionsonRobotics, 30(2):289–309,2013. [3] S.Caldera,A.Rassau, andD.Chai. Reviewofdeep learning methods in robotic grasp detection. Multi- modalTechnologiesand Interaction, 2(3):57,2018. [4] F.-J. Chu, R. Xu, and P. A. Vela. Real-world mul- tiobject, multigrasp detection. IEEE Robotics and AutomationLetters, 3(4):3355–3362,2018. [5] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical im- age database. In Proceedings of the IEEE Confer- ence on Computer Vision and Pattern Recognition (CVPR), pages248–255,2009. [6] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pat- ternRecognition (CVPR), pages 770–778,2016. [7] S. Kumra and C. Kanan. Robotic grasp detection using deep convolutional neural networks. In 2017 IEEE/RSJ International Conference on Intelligent RobotsandSystems (IROS), pages769–776,2017. [8] T.-C. Lee, R. L. Kashyap, and C.-N. Chu. Building skeletonmodelsvia3-dmedialsurfaceaxis thinning algorithms. CVGIP: Graphical Models and Image Processing, 56(6):462–478,1994. [9] I. Lenz, H. Lee, and A. Saxena. Deep learning for detecting robotic grasps. The International Journal ofRoboticsResearch, 34(4-5):705–724, 2015. [10] S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen. Learning hand-eye coordination for robotic grasping with deep learning and large- scale data collection. The International Journal of RoboticsResearch, 37(4-5):421–436,2018. [11] K.-K. Maninis, S. Caelles, J. Pont-Tuset, and L. Van Gool. Deep extreme cut: From extreme points toobject segmentation. InProceedingsof the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 616–625,2018. [12] L.PintoandA.Gupta. Supersizingself-supervision: Learning to grasp from 50k tries and 700 robot hours. In 2016 International Conference on RoboticsandAutomation(ICRA),pages3406–3413. IEEE,2016. [13] J.RedmonandA.Angelova. Real-timegraspdetec- tion using convolutional neural networks. In 2015 International Conference on Robotics and Automa- tion (ICRA), pages 1316–1322. IEEE,2015. [14] S.Ren,K.He,R.Girshick, andJ.Sun. Faster r-cnn: Towards real-time object detection with region pro- posal networks. In Advances in neural information processing systems, pages 91–99,2015. [15] C. Rother, V. Kolmogorov, and A. Blake. ” grab- cut” interactive foreground extraction using iterated graph cuts. ACM transactions on graphics (TOG), 23(3):309–314,2004. [16] A. Saxena, J. Driemeyer, and A. Y. Ng. Robotic grasping of novel objects using vision. The Interna- tionalJournalofRoboticsResearch,27(2):157–173, 2008. [17] M. Suchi, T. Patten, D. Fischinger, and M. Vincze. Easylabel: A semi-automatic pixel-wise object an- notation tool for creating robotic rgb-d datasets. In 2019 International Conference on Robotics and Au- tomation (ICRA), pages6678–6684. IEEE,2019. [18] A.Zeng,S.Song,K.-T.Yu,E.Donlon,F.R.Hogan, M. Bauza, D. Ma, O. Taylor, M. Liu, E. Romo, etal.Roboticpick-and-placeofnovelobjectsinclut- terwithmulti-affordancegraspingandcross-domain image matching. In 2018 International Conference on Robotics and Automation (ICRA), pages 1–8. IEEE,2018. [19] A. Zeng, K.-T. Yu, S. Song, D. Suo, E. Walker, A. Rodriguez, and J. Xiao. Multi-view self- supervised deep learning for 6d pose estimation in theamazonpickingchallenge. In2017International Conference on Robotics and Automation (ICRA), pages 1386–1383. IEEE,2017. [20] X. Zhou, X. Lan, H. Zhang, Z. Tian, Y. Zhang, and N. Zheng. Fully convolutional grasp detection net- work with oriented anchor box. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages7223–7230,2018. 130
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Joint Austrian Computer Vision and Robotics Workshop 2020
Title
Joint Austrian Computer Vision and Robotics Workshop 2020
Editor
Graz University of Technology
Location
Graz
Date
2020
Language
English
License
CC BY 4.0
ISBN
978-3-85125-752-6
Size
21.0 x 29.7 cm
Pages
188
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Joint Austrian Computer Vision and Robotics Workshop 2020