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检索条件"机构=Computational Learning and Motor Control Laboratory in Computer Science"
20 条 记 录,以下是1-10 订阅
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Reinforcement learning of motor skills in high dimensions: A path integral approach
Reinforcement learning of motor skills in high dimensions: A...
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IEEE International Conference on Robotics and Automation (ICRA)
作者: Evangelos Theodorou Jonas Buchli Stefan Schaal Computational Learning and Motor Control Laboratory in Computer Science Biomedical Engineering Neuroscience University of Southern California USA
Reinforcement learning (RL) is one of the most general approaches to learning control. Its applicability to complex motor systems, however, has been largely impossible so far due to the computational difficulties that... 详细信息
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Stochastic differential dynamic programming
Stochastic differential dynamic programming
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作者: Theodorou, Evangelos Tassa, Yuval Todorov, Emo Departments of Computer Science and Neuroscience Computational Learning and Motor Control Lab. University of Southern California United States Interdisciplinary Center for Neural Computation Hebrew University Jerusalem Israel Department of Computer Science and Engineering University of Washington Seattle WA United States
Although there has been a significant amount of work in the area of stochastic optimal control theory towards the development of new algorithms, the problem of how to control a stochastic nonlinear system remains an o... 详细信息
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Movement segmentation and recognition for imitation learning  15
Movement segmentation and recognition for imitation learning
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15th International Conference on Artificial Intelligence and Statistics, AISTATS 2012
作者: Meier, Franziska Theodorou, Evangelos Schaal, Stefan Computational Learning and Motor Control Lab University of Southern California Los Angeles United States Department of Computer Science and Engineering University of Washington Seattle United States Max-Planck-Institute for Intelligent Systems Tübingen Germany
In human movement learning, it is most common to teach constituent elements of complex movements in isolation, before chaining them into complex movements. Segmentation and recognition of observed movement could thus ... 详细信息
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Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization
Virtual vs. real: Trading off simulations and physical exper...
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2017 IEEE International Conference on Robotics and Automation, ICRA 2017
作者: Marco, Alonso Berkenkamp, Felix Hennig, Philipp Schoellig, Angela P. Krause, Andreas Schaal, Stefan Trimpe, Sebastian Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science ETH Zurich Switzerland Canada Computational Learning and Motor Control Lab University of Southern California United States Max Planck ETH Center for Learning Systems Tubingen Germany Zurich Switzerland
In practice, the parameters of control policies are often tuned manually. This is time-consuming and frustrating. Reinforcement learning is a promising alternative that aims to automate this process, yet often require... 详细信息
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Editorial
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International Journal of Humanoid Robotics 2005年 第4期2卷 389-390页
作者: CHENG, GORDON SCHAAL, STEFAN ATKESON, CHRISTOPHER G. JST-ICORP Computational Brain Project ATR Computational Neuroscience Laboratory 2-2-2 Keihanna Science City Soraku-gun Kyoto619-0288 Japan Computational Learning and Motor Control Lab University of Southern California Hedco Neurosciences Building HNB-103 3641 Watt Way Los AngelesCA90089-2520 United States Robotics Institute Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States
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Toward simple control for complex, autonomous robotic applications: Combining discrete and rhythmic motor primitives
Toward simple control for complex, autonomous robotic applic...
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作者: Degallier, Sarah Righetti, Ludovic Gay, Sebastien Ijspeert, Auke CNBI Laboratory School of Engineering EPFL Ecole Polytechnique Fédérale de Lausanne Lausanne 1015 Switzerland Biorobotics Laboratory School of Engineering EPFL Ecole Polytechnique Fédérale de Lausanne Lausanne 1015 Switzerland Computational Learning and Motor Control Lab Computer Science Neurosciences and Biomedical Engineering University of Southern California Los Angeles CA 90089 United States
Vertebrates are able to quickly adapt to new environments in a very robust, seemingly effortless way. To explain both this adaptivity and robustness, a very promising perspective in neurosciences is the modular approa... 详细信息
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learning movement primitives
Springer Tracts in Advanced Robotics
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Springer Tracts in Advanced Robotics 2005年 15卷 561-572页
作者: Schaal, Stefan Peters, Jan Nakanishi, Jun Ijspeert, Auke Computational Learning and Motor Control Laboratory Computer Science and Neuroscience University of Southern California Los Angeles CA 90089-2520 United States Dept of Humanoid Robotics and Computational Neuroscience ATR Computational Neuroscience Laboratory 2-2-2 Hikaridai Seika-cho Soraku-gun 619-0288 Kyoto Japan School of Computer and Communication Sciences EPFL Swiss Federal Institute of Technology Lausanne CH 1015 Lausanne Switzerland
This paper discusses a comprehensive framework for modular motor control based on a recently developed theory of dynamic movement primitives (DMP). DMPs are a formulation of movement primitives with autonomous nonline... 详细信息
来源: 评论
Inverse kinematics for humanoid robots
Inverse kinematics for humanoid robots
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IEEE International Conference on Robotics and Automation (ICRA)
作者: G. Tevatia S. Schaal Kawato Dynamic Brain Project ERATO Japan Science and Technology Agency Kyoto Japan Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA
Real-time control of the end-effector of a humanoid robot in external coordinates requires computationally efficient solutions of the inverse kinematics problem. In this context, this paper investigates methods of res... 详细信息
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Dependence of orientation tuning on recurrent excitation and inhibition in a network model of V1
Dependence of orientation tuning on recurrent excitation and...
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22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
作者: Wimmer, Klaus Stimberg, Marcel Martin, Robert Schwabe, Lars Mariño, Jorge Schummers, James Lyon, David C. Sur, Mriganka Obermayer, Klaus Bernstein Center for Computational Neuroscience Technische Universität Berlin Germany Dept of Computer Science and Electrical Engineering University of Rostock Germany Dept of Medicine Neuroscience and Motor Control Group Univ. A Coruña Spain Dept of Brain and Cognitive Sci Picower Ctr for Learning and Memory MIT Cambridge United States Dept of Anatomy and Neurobiology University of California Irvine United States
The computational role of the local recurrent network in primary visual cortex is still a matter of debate. To address this issue, we analyze intracellular recording data of cat V1, which combine measuring the tuning ... 详细信息
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Stochastic Differential Dynamic Programming
Stochastic Differential Dynamic Programming
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American control Conference
作者: Evangelos Theodorou Yuval Tassa Emo Todorov Computational Learning and Motor Control Lab Departments of Computer Science and Neuroscience University of Southern California Interdisciplinary Center for Neural Computation Hebrew University Jerusalem Israel Department of Computer Science and Engineering and the Department of Applied Mathematics University of Washington Seattle WA
Although there has been a significant amount of work in the area of stochastic optimal control theory towards the development of new algorithms, the problem of how to control a stochastic nonlinear system remains an o... 详细信息
来源: 评论