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We tackle the problem of generalization to unseen configurations for dynamic tasks in the real world while learning from high-dimensional image input.
From implicit skills to explicit knowledge: A bottom-up model of skill learning
Ron Sun, Edward Merrill, and Todd Peterson · 2001
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Learning options in reinforcement learning
Martin Stolle and Doina Precup · 2002
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Model compression
Cristian Bucilua, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Dynamic movement primitives-a framework for motor control in humans and humanoid robotics
Stefan Schaal · 2006
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Learning motor primitives for robotics
Jens Kober and Jan Peters · 2009
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Learning and generalization of motor skills by learning from demonstration
Peter Pastor, Heiko Hoffmann, Tamim Asfour, and Stefan Schaal · 2009
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Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies
Sylvain Calinon, Irene Sardellitti, and Darwin G. Caldwell · 2010
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Robot motor skill coordination with em-based reinforcement learning
Petar Kormushev, Sylvain Calinon, and Darwin G Caldwell · 2010
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Task-specific generalization of discrete and periodic dynamic movement primitives
Aleš Ude, Andrej Gams, Tamim Asfour, and Jun Morimoto · 2010
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Reinforcement learning to adjust robot movements to new situations
Jens Kober, Erhan Oztop, and Jan Peters · 2011
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Skill learning and task outcome prediction for manipulation
Peter Pastor, Mrinal Kalakrishnan, Sachin Chitta, Evangelos Theodorou, and Stefan Schaal · 2011
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Reinforcement learning with sequences of motion primitives for robust manipulation
F. Stulp, E. A. Theodorou, and S. Schaal · 2012
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MuJoCo: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Dynamical movement primitives: Learning attractor models for motor behaviors
Auke Jan Ijspeert, Jun Nakanishi, Heiko Hoffmann, Peter Pastor, and Stefan Schaal · 2013
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Guided policy search
Sergey Levine and Vladlen Koltun · 2013
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Learning to select and generalize striking movements in robot table tennis
Katharina Mülling, Jens Kober, Oliver Kroemer, and Jan Peters · 2013
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Dynamic movement primitives for human-robot interaction: Comparison with human behavioral observation
M. Prada, A. Remazeilles, A. Koene, and S. Endo · 2013
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Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems
Elmar Rückert and Andrea d’Avella · 2013
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Reinforcement learning vs human programming in tetherball robot games
Simone Parisi, Hany Abdulsamad, Alexandros Paraschos, Christian Daniel, and Jan Peters · 2015
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Andrei A Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, and Raia Hadsell · 2015
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Feudal networks for hierarchical reinforcement learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu · 2017
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
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Pytorch implementations of reinforcement learning algorithms
Ilya Kostrikov · 2018
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Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang Gu, Honglak Lee, and Sergey Levine · 2018
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Deep encoder-decoder networks for mapping raw images to dynamic movement primitives
Rok Pahic, Andrej Gams, Aleš Ude, and Jun Morimoto · 2018
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Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agrawal, Ashvin Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
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A tutorial on task-parameterized movement learning and retrieval
Sylvain Calinon · 2016
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Dynamic movement primitives in latent space of time-dependent variational autoencoders
Nutan Chen, Maximilian Karl, and Patrick Van Der Smagt · 2016
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Hierarchical relative entropy policy search
Christian Daniel, Gerhard Neumann, Oliver Kroemer, and Jan Peters · 2016
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Lerrel Pinto and Abhinav Gupta · 2016
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Divide-and-conquer reinforcement learning
Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, and Sergey Levine · 2017
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Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, and Ken Goldberg · 2017
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Nathan D Ratliff, Jan Issac, Daniel Kappler, Stan Birchfield, and Dieter Fox · 2018
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Active learning of probabilistic movement primitives
Adam Conkey and Tucker Hermans · 2019
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Kernelized movement primitives
Yanlong Huang, Leonel Rozo, João Silvério, and Darwin G Caldwell · 2019
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Variable impedance control in end-effector space: An action space for reinforcement learning in contact-rich tasks
Roberto Martin-Martin, Michelle A. Lee, Rachel Gardner, Silvio Savarese, Jeannette Bohg, and Animesh Garg · 2019
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William Whitney, Rajat Agarwal, Kyunghyun Cho, and Abhinav Gupta · 2019
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Neural dynamic policies for end-to-end sensorimotor learning
Shikhar Bahl, Mustafa Mukadam, Abhinav Gupta, and Deepak Pathak · 2020
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Rmpflow: A computational graph for automatic motion policy generation
Ching-An Cheng, Mustafa Mukadam, Jan Issac, Stan Birchfield, Dieter Fox, Byron Boots, and Nathan Ratliff · 2020
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A modular robotic arm control stack for research: Franka-interface and frankapy
Kevin Zhang, Mohit Sharma, Jacky Liang, and Oliver Kroemer · 2020
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robosuite: A modular simulation framework and benchmark for robot learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, and Roberto Martín-Martín · 2020
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