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Learning Continuous Muscle Control for a Multi-joint Arm by Extending Proximal Policy Optimization with a Liquid State Machine.

J. Tieck, M. Pogancic, J. Kaiser, A. Rönnau, M. Gewaltig, and R. Dillmann. ICANN (1), volume 11139 of Lecture Notes in Computer Science, page 211-221. Springer, (2018)

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A multi-resolution 3-D environment model for autonomous planetary exploration.J. Oberländer, S. Klemm, G. Heppner, A. Rönnau, and R. Dillmann. CASE, page 229-235. IEEE, (2014)Microsaccades for Neuromorphic Stereo Vision.J. Kaiser, J. Weinland, P. Keller, L. Steffen, J. Tieck, D. Reichard, A. Rönnau, J. Conradt, and R. Dillmann. ICANN (1), volume 11139 of Lecture Notes in Computer Science, page 244-252. Springer, (2018)Efficient, Collaborative Screw Assembly in a Shared Workspace.C. Jülg, A. Hermann, A. Rönnau, and R. Dillmann. IAS, volume 867 of Advances in Intelligent Systems and Computing, page 837-848. Springer, (2018)Towards Grasping with Spiking Neural Networks for Anthropomorphic Robot Hands.J. Tieck, H. Donat, J. Kaiser, I. Peric, S. Ulbrich, A. Rönnau, J. Zöllner, and R. Dillmann. ICANN (1), volume 10613 of Lecture Notes in Computer Science, page 43-51. Springer, (2017)Spiking Convolutional Deep Belief Networks.J. Kaiser, D. Zimmerer, J. Tieck, S. Ulbrich, A. Rönnau, and R. Dillmann. ICANN (2), volume 10614 of Lecture Notes in Computer Science, page 3-11. Springer, (2017)Towards a framework for end-to-end control of a simulated vehicle with spiking neural networks.J. Kaiser, J. Tieck, C. Hubschneider, P. Wolf, M. Weber, M. Hoff, A. Friedrich, K. Wojtasik, A. Rönnau, R. Kohlhaas and 2 other author(s). SIMPAR, page 127-134. IEEE, (2016)Transparent Robot Behavior by Adding Intuitive Visual and Acoustic Feedback to Motion Replanning.G. Bolano, A. Rönnau, and R. Dillmann. RO-MAN, page 1075-1080. IEEE, (2018)Risk Aware Robots - Health Estimation and Capability Selection.G. Heppner, C. Plasberg, L. Puck, T. Schnell, T. Büttner, A. Rönnau, and R. Dillmann. CASE, page 1193-1199. IEEE, (2019)Learning Continuous Muscle Control for a Multi-joint Arm by Extending Proximal Policy Optimization with a Liquid State Machine.J. Tieck, M. Pogancic, J. Kaiser, A. Rönnau, M. Gewaltig, and R. Dillmann. ICANN (1), volume 11139 of Lecture Notes in Computer Science, page 211-221. Springer, (2018)Creating an Obstacle Memory Through Event-Based Stereo Vision and Robotic Proprioception.L. Steffen, B. Hauck, J. Kaiser, J. Weinland, S. Ulbrich, D. Reichard, A. Rönnau, and R. Dillmann. CASE, page 1829-1836. IEEE, (2019)

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Learning Continuous Muscle Control for a Multi-joint Arm by Extending Proximal Policy Optimization with a Liquid State Machine.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Learning Continuous Muscle Control for a Multi-joint Arm by Extending Proximal Policy Optimization with a Liquid State Machine.

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Learning Continuous Muscle Control for a Multi-joint Arm by Extending Proximal Policy Optimization with a Liquid State Machine.