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Learning CPG-based biped locomotion with a policy gradient method.

T. Matsubara, J. Morimoto, J. Nakanishi, M. aki Sato, and K. Doya. Humanoids, page 208-213. IEEE, (2005)

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Learning CPG Sensory Feedback with Policy Gradient for Biped Locomotion for a Full-Body Humanoid.G. Endo, J. Morimoto, T. Matsubara, J. Nakanishi, and G. Cheng. AAAI, page 1267-1273. AAAI Press / The MIT Press, (2005)Reinforcement learning of clothing assistance with a dual-arm robot.T. Tamei, T. Matsubara, A. Rai, and T. Shibata. Humanoids, page 733-738. IEEE, (2011)Highly Precise Dynamic Simulation Environment for Humanoid Robots.J. Hale, B. Hohl, S. Hyon, T. Matsubara, E. Moraud, and G. Cheng. Advanced Robotics, 22 (10): 1075-1105 (2008)Input-Output Manifold Learning with State Space Models.D. Tanaka, T. Matsubara, and K. Sugimoto. IEICE Transactions, 99-A (6): 1179-1187 (2016)Dynamic Linear Bellman Combination of Optimal Policies for Solving New Tasks.T. Matsubara, T. Asakura, and K. Sugimoto. IEICE Transactions, 98-A (10): 2187-2190 (2015)Policy Transfer from Simulations to Real World by Transfer Component Analysis.T. Matsubara, Y. Norinaga, Y. Ozawa, and Y. Cui. CASE, page 264-269. IEEE, (2018)Multimodal Policy Search using Overlapping Mixtures of Sparse Gaussian Process Prior.H. Sasaki, and T. Matsubara. ICRA, page 2433-2439. IEEE, (2019)Learning Basis Representations of Inverse Dynamics Models for Real-Time Adaptive Control.Y. Horiguchi, T. Matsubara, and M. Kidode. ICONIP (2), volume 6444 of Lecture Notes in Computer Science, page 668-675. Springer, (2010)Model-based reinforcement learning approach for deformable linear object manipulation.H. Han, G. Paul, and T. Matsubara. CASE, page 750-755. IEEE, (2017)Alleviating parameter-tuning burden in reinforcement learning for large-scale process control.L. Zhu, G. Takami, M. Kawahara, H. Kanokogi, and T. Matsubara. Comput. Chem. Eng., (2022)

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Learning CPG-based biped locomotion with a policy gradient method.

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Disambiguation of "Matsubara, Takamitsu"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Learning CPG-based biped locomotion with a policy gradient method.

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Learning CPG-based biped locomotion with a policy gradient method.