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检索条件"机构=Computational Learning and Motor Control"
70 条 记 录,以下是11-20 订阅
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Hierarchical reinforcement learning with movement primitives
Hierarchical reinforcement learning with movement primitives
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IEEE-RAS International Conference on Humanoid Robots
作者: Freek Stulp Stefan Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA
Temporal abstraction and task decomposition drastically reduce the search space for planning and control, and are fundamental to making complex tasks amenable to learning. In the context of reinforcement learning, tem... 详细信息
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Compliant quadruped locomotion over rough terrain
Compliant quadruped locomotion over rough terrain
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2009 IEEE/RSJ International Conference on Intelligent Robots and Systems
作者: Jonas Buchli Mrinal Kalakrishnan Michael Mistry Peter Pastor Stefan Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA
Many critical elements for statically stable walking for legged robots have been known for a long time, including stability criteria based on support polygons, good foothold selection, recovery strategies to name a fe... 详细信息
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learning to Grasp under Uncertainty
Learning to Grasp under Uncertainty
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2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
作者: Freek Stulp Evangelos Theodorou Jonas Buchli Stefan Schaal Computational Learning and Motor Control Lab University of Southern CaliforniaLos AngelesCA 90089
We present an approach that enables robots to learn motion primitives that are robust towards state estimation uncertainties. During reaching and preshaping, the robot learns to use fine manipulation strategies to man... 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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Compact models of motor primitive variations for predictable reaching and obstacle avoidance
Compact models of motor primitive variations for predictable...
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9th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS09
作者: Stulp, Freek Oztop, Erhan Pastor, Peter Beetz, Michael Schaaz, Stefan Computational Learning and Motor Control Lab. University of Southern California Los Angeles CA United States Kyoto Japan Computational Neuroscience Laboratories Advanced Telecommunications Research Institute International Kyoto Japan Intelligent Autonomous Systems Group Technische Universitdt Munchen Munich Germany
In most activities of daily living, related tasks are encountered over and over again. This regularity allows humans and robots to reuse existing solutions for known recurring tasks. We expect that reusing a set of st... 详细信息
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learning motion primitive goals for robust manipulation
Learning motion primitive goals for robust manipulation
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2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
作者: Freek Stulp Evangelos Theodorou Mrinal Kalakrishnan Peter Pastor Ludovic Righetti Stefan Schaal Computational Learning and Motor Control laboratory University of Southern California Los Angeles CA USA
Applying model-free reinforcement learning to manipulation remains challenging for several reasons. First, manipulation involves physical contact, which causes discontinuous cost functions. Second, in manipulation, th... 详细信息
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Movement segmentation using a primitive library
Movement segmentation using a primitive library
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2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
作者: Franziska Meier Evangelos Theodorou Freek Stulp Stefan Schaal Computational Learning and Motor Control laboratory University of Southern California Los Angeles CA USA
Segmenting complex movements into a sequence of primitives remains a difficult problem with many applications in the robotics and vision communities. In this work, we show how the movement segmentation problem can be ... 详细信息
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An Iterative Path Integral Stochastic Optimal control Approach for learning Robotic Tasks
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IFAC Proceedings Volumes 2011年 第1期44卷 11594-11601页
作者: Evangelos Theodorou Freek Stulp Jonas Buchli Stefan Schaal Computational Learning and Motor Control Lab University of Southern California USA Department of Advanced Robotics Italian Institute of Technology ATR Computational Neuroscience Laboratories Kyoto 619-0288 Japan
Abstract Recent work on path integral stochastic optimal control theory Theodorou et al. (2010a); Theodorou (2011) has shown promising results in planning and control of nonlinear systems in high dimensional state spa... 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
来源: 评论