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[4] A. Boularias, J. A. Bagnell, and A. Stentz. Learn- ing to manipulate unknown objects in clutter by re- inforcement. In Proc. of AAAI Conf. on Artificial Intelligence, pages1336–1342,2015. [5] S. Calinon, P. Evrard, E. Gribovskaya, A. Billard, and A. Kheddar. Learning collaborative manipula- tion tasks by demonstration using a haptic interface. In Proc. of Int. Conf. on Advanced Robotics, pages 1–6,2009. [6] Y. Duan, M. Andrychowicz, B. Stadie, O. Jonathan Ho, J. Schneider, I. Sutskever, P. Abbeel, and W. Zaremba. One-shot imitation learning. In Advances in Neural Information Processing Systems30, pages1087–1098,2017. [7] F.Gouidis,P.Panteleris, I.Oikonomidis, andA.Ar- gyros. Accurate hand keypoint localization on mo- biledevices. InProc.of IEEEInt.Conf.onMachine Vision Applications, 2019. [8] M. Hirschmanner, C. Tsiourti, T. Patten, and M. Vincze. Virtual reality teleoperation of a hu- manoid robot using markerless human upper body pose imitation. In Proc. of IEEE-RAS Int. Conf. on Humanoid Robots, 2019. [9] D. Huang, S. Nair, D. Xu, Y. Zhu, A. Garg, L. Fei- Fei, S. Savarese, and J. C. Niebles. Neural task graphs: Generalizing to unseen tasks from a single video demonstration. In Proc. of IEEE/CVF Conf. on Computer Vision and Pattern Recognition, pages 8557–8566,2019. [10] S. James, M. Bloesch, and A. J. Davison. Task- embedded control networks for few-shot imitation learning. InProc.ofConf.onRobotLearning,pages 783–795, 2018. [11] D.Kalashnikov,A. Irpan,P.Pastor, J. Ibarz,A.Her- zog,E. Jang,D.Quillen,E.Holly,M.Kalakrishnan, V. Vanhoucke, and S. Levine. Scalable deep rein- forcement learning for vision-based robotic manip- ulation. In Proc. of Conf. on Robot Learning, pages 651–673, 2018. [12] G. Konidaris, S. Kuindersma, R. Grupen, and A. Barto. Robot learning from demonstration by constructingskill trees. TheInt. JournalofRobotics Research, 31(3):360–375,2012. [13] V. KruΒ¨ger, D. L. Herzog, S. Baby, A. Ude, and D. Kragic. Learning actions from observations. IEEE Robotics Automation Magazine, 17(2):30–43, 2010. [14] S.Levine,C.Finn,T.Darrell,andP.Abbeel.End-to- end training of deep visuomotor policies. J. Mach. Learn.Res., 17(1):13341373, Jan.2016. [15] 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 Int. Journal of Robotics Re- search, 47(4-5):421–436,2018. [16] Y. Liu, A. Gupta, P. Abbeel, and S. Levine. Imita- tionfromobservation: Learningto imitatebehaviors from raw video via context translation. In Proc. of IEEE Int. Conf. on Robotics and Automation, pages 1118–1125,2018. [17] P.Panteleris, I.Oikonomidis,andA.Argyros. Using a single RGB frame for real time 3D hand pose es- timation in the wild. In Proc. of IEEE Winter Conf. onApplicationsofComputerVision,pages436–445, 2018. [18] A. Pashevich, R. Strudel, I. Kalevatykh, I. Laptev, and C. Schmid. Learning to augment synthetic images for Sim2Real policy transfer. In Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Sys- tems, pages2651–2657,2019. [19] P. Pastor, L. Righetti, M. Kalakrishnan, and S. Schaal. Online movement adaptation based on previous sensor experiences. In Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pages 365–371,2011. [20] R. Rahmatizadeh, P. Abolghasemi, L. Blni, and S.Levine. Vision-basedmulti-taskmanipulation for inexpensive robots using end-to-end learning from demonstration. In Proc. of IEEE Int. Conf. on RoboticsandAutomation, pages3758–3765,2018. [21] S. Schaal. Is imitation learning the route to hu- manoid robots? Trends in cognitive sciences, 3(6):233–242, 1999. [22] M. Schneider and W. Ertel. Robot learning by demonstration with local Gaussian process regres- sion. In Proc. of IEEE/RSJ Int. Conf. on Intelligent Robotsand Systems, pages255–260,2010. [23] G. Schreiber, A. Stemmer, and R. Bischoff. The fast research interface for the KUKA lightweight robot. In IEEE ICRA 2010 Workshop on Inno- vative Robot Control Architectures for Demanding (Research) Applications – How to Modify and En- hanceCommercialControllers, 2010. [24] P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, and S. Levine. Time-contrastive net- works: Self-supervised learning from video. In Proc. of IEEE Int. Conf. on Robotics and Automa- tion, pages1134–1141, 2018. [25] D.Victor. Handtrack: Alibraryforprototypingreal- time hand tracking interfaces using convolutional neuralnetworks. GitHubrepository, 2017. [26] T. Yu, C. Finn, S. Dasari, A. Xie, T. Zhang, P. Abbeel, and S. Levine. One-shot imitation from observing humans via domain-adaptive meta- learning. InProc.ofRobotics: ScienceandSystems, 2018. [27] T. Zhang, Z. McCarthy, O. Jow, D. Lee, X. Chen, K. Goldberg, and P. Abbeel. Deep imitation learn- ing for complex manipulation tasks from virtual re- ality teleoperation. In Proc. of IEEE Int. Conf. on RoboticsandAutomation, pages5628–5635,2018. 47
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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