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For robots operating in the real world, it is desirable to learn reusable behaviours that can effectively be transferred and adapted to numerous tasks and scenarios.
Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
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Learning table tennis with a mixture of motor primitives
Katharina Muelling, Jens Kober, and Jan Peters · 2010
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Accelerating reinforcement learning with learned skill priors
Karl Pertsch, Youngwoon Lee, and Joseph J Lim · 2010
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Clustering via dirichlet process mixture models for portable skill discovery
Scott Niekum and Andrew G Barto · 2011
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Hierarchical relative entropy policy search
Christian Daniel, Gerhard Neumann, and Jan Peters · 2012
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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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Probabilistic segmentation applied to an assembly task
Rudolf Lioutikov, Gerhard Neumann, Guilherme Maeda, and Jan Peters · 2015
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Nonparametric bayesian reward segmentation for skill discovery using inverse reinforcement learning
Pravesh Ranchod, Benjamin Rosman, and George Konidaris · 2015
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Extracting low-dimensional control variables for movement primitives
Elmar Rueckert, Jan Mundo, Alexandros Paraschos, Jan Peters, and Gerhard Neumann · 2015
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Learning and transfer of modulated locomotor controllers
Nicolas Heess, Greg Wayne, Yuval Tassa, Timothy Lillicrap, Martin Riedmiller, and David Silver · 2016
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Efficient unsupervised temporal segmentation of motion data
Björn Krüger, Anna Vögele, Tobias Willig, Angela Yao, Reinhard Klein, and Andreas Weber · 2016
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The option-critic architecture
Pierre-Luc Bacon, Jean Harb, and Doina Precup · 2017
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Stochastic neural networks for hierarchical reinforcement learning
Carlos Florensa, Yan Duan, and Pieter Abbeel · 2017
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Variational intrinsic control
Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 2017
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Learning movement primitive libraries through probabilistic segmentation
Rudolf Lioutikov, Gerhard Neumann, Guilherme Maeda, and Jan Peters · 2017
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Asymmetric actor critic for image-based robot learning
Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel · 2017
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Maximum a posteriori policy optimisation
Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, and Martin Riedmiller · 2018
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Dimensionality reduction in learning gaussian mixture models of movement primitives for contextualized action selection and adaptation
Adrià Colomé and Carme Torras · 2018
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Latent space policies for hierarchical reinforcement learning
Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, and Sergey Levine · 2018
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Learning an embedding space for transferable robot skills
Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, and Martin Riedmiller · 2018
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Task-Embedded control networks for Few-Shot imitation learning
Stephen James, Michael Bloesch, and Andrew J Davison · 2018
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Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang Gu, Honglak Lee, and Sergey Levine · 2018
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Using probabilistic movement primitives in robotics
Dynamics-aware unsupervised discovery of skills
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman · 2019
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Exploiting hierarchy for learning and transfer in kl-regularized rl
D Tirumala, H Noh, A Galashov, L Hasenclever, and others · 2019
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Catch & carry: Reusable neural controllers for vision-guided Whole-Body tasks
Josh Merel, Saran Tunyasuvunakool, Arun Ahuja, Yuval Tassa, Leonard Hasenclever, Vu Pham, Tom Erez, Greg Wayne, and Nicolas Heess · 2020
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Alexandros Paraschos, Christian Daniel, Jan Peters, and Gerhard Neumann · 2018
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Learning task-parameterized dynamic movement primitives using mixture of gmms
Affan Pervez and Dongheui Lee · 2018
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2018
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Learning by playing solving sparse reward tasks from scratch
Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom van de Wiele, Vlad Mnih, Nicolas Heess, and Jost Tobias Springenberg · 2018
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Taco: Learning task decomposition via temporal alignment for control
Kyriacos Shiarlis, Markus Wulfmeier, Sasha Salter, Shimon Whiteson, and Ingmar Posner · 2018
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Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2019
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Information asymmetry in KL-regularized RL
Alexandre Galashov, Siddhant Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojtek M Czarnecki, Yee Whye Teh, Razvan Pascanu, and Nicolas Heess · 2019
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Learning to combine primitive skills: A step towards versatile robotic manipulation
Robin A M Strudel, Alexander Pashevich, Igor Kalevatykh, Ivan Laptev, Josef Sivic, and Cordelia Schmid · 2020
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Critic regularized regression
Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh S Merel, Jost Tobias Springenberg, Scott E Reed, Bobak Shahriari, Noah Siegel, Caglar Gulcehre, Nicolas Heess, et al · 2020
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Compositional transfer in hierarchical reinforcement learning
Markus Wulfmeier, Abbas Abdolmaleki, Roland Hafner, Jost Tobias Springenberg, Michael Neunert, Tim Hertweck, Thomas Lampe, Noah Siegel, Nicolas Heess, and Martin A Riedmiller · 2020
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Hierarchical reinforcement learning by discovering intrinsic options
Jesse Zhang, Haonan Yu, and Wei Xu · 2020
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OPAL: Offline primitive discovery for accelerating offline reinforcement learning
Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, and Ofir Nachum · 2021
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Accelerating robotic reinforcement learning via parameterized action primitives
Murtaza Dalal, Deepak Pathak, and Ruslan Salakhutdinov · 2021
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Beyond pick-and-place: Tackling robotic stacking of diverse shapes
Alex X. Lee, Coline Manon Devin, Yuxiang Zhou, Thomas Lampe, Jost Tobias Springenberg, Konstantinos Bousmalis, Arunkumar Byravan, Abbas Abdolmaleki, Nimrod Gileadi, David Khosid, Claudio Fantacci, Jose Enrique Chen, Akhil Raju, Rae Jeong, Michael Neunert, Antoine Laurens, Stefano Saliceti, Federico Casarini, Martin Riedmiller, Raia Hadsell, and Francesco Nori · 2021
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Demonstration-Guided reinforcement learning with learned skills
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Skid raw: Skill discovery from raw trajectories
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Contextual latent-movements off-policy optimization for robotic manipulation skills
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Data-efficient hindsight off-policy option learning
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