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Robots need to learn skills that can not only generalize across similar problems but also be directed to a specific goal.
Goal-conditioned imitation learning
Yiming Ding, Carlos Florensa, Mariano Phielipp, and Pieter Abbeel · 1906
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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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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Learning parameterized skills
Bruno da Silva, George Konidaris, and Andrew Barto · 2012
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Reinforcement learning to adjust parametrized motor primitives to new situations
Jens Kober, Andreas Wilhelm, Erhan Oztop, and Jan Peters · 2012
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Data-efficient generalization of robot skills with contextual policy search
Andras Gabor Kupcsik, Marc Peter Deisenroth, Jan Peters, and Gerhard Neumann · 2013
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Learning to select and generalize striking movements in robot table tennis
Katharina Muelling, Jens Kober, Oliver Kroemer, and Jan Peters · 2013
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Learning compact parameterized skills with a single regression
Freek Stulp, Gennaro Raiola, Antoine Hoarau, Serena Ivaldi, and Olivier Sigaud · 2013
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Learning parameterized motor skills on a humanoid robot
Bruno da Silva, Gianluca Baldassarre, George Konidaris, and Andrew Barto · 2014
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Multi-task policy search for robotics
Marc Peter Deisenroth, Peter Englert, Jan Peters, and Dieter Fox · 2014
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Deep spatial autoencoders for visuomotor learning
Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, and Pieter Abbeel · 2015
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Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
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Learning modular neural network policies for multi-task and multi-robot transfer
Coline Devin, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, and Sergey Levine · 2016
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Using task features for zero-shot knowledge transfer in lifelong learning
David Isele, Mohammad Rostami, and Eric Eaton · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Deep imitation learning for complex manipulation tasks from virtual reality teleoperation
Tianhao Zhang, Zoe McCarthy, Owen Jowl, Dennis Lee, Xi Chen, Ken Goldberg, and Pieter Abbeel · 2017
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End-to-end driving via conditional imitation learning
Felipe Codevilla, Matthias Muller, Antonio López, Vladlen Koltun, and Alexey Dosovitskiy · 2018
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Learning actionable representations with goal-conditioned policies
Dibya Ghosh, Abhishek Gupta, and Sergey Levine · 2018
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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
Sergey Levine, Peter Pastor, Alex Krizhevsky, Julian Ibarz, and Deirdre Quillen · 2018
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Pay attention! - robustifying a deep visuomotor policy through task-focused visual attention
Pooya Abolghasemi, Amir Mazaheri, Mubarak Shah, and Ladislau Boloni · 2019
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Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 2017
Cited alongside, same era.
Automatic goal generation for reinforcement learning agents
David Held, Xinyang Geng, Carlos Florensa, and Pieter Abbeel · 2017
Cited alongside, same era.
Preparing for the unknown: Learning a universal policy with online system identification
Wenhao Yu, C. Karen Liu, and Greg Turk · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine
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One-shot visual imitation learning via meta-learning
Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine
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Stochastic gradient methods with layer-wise adaptive moments for training of deep networks
Boris Ginsburg, Patrice Castonguay, Oleksii Hrinchuk, Oleksii Kuchaiev, Vitaly Lavrukhin, Ryan Leary, Jason Li, Huyen Nguyen, and Jonathan M. Cohen · 2019
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End-to-end robotic reinforcement learning without reward engineering
Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, and Sergey Levine · 2019
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A practical approach to insertion with variable socket position using deep reinforcement learning
Mel Vecerik, Oleg Sushkov, David Barker, Thomas Rothörl, Todd Hester, and Jon Scholz · 2019
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