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We tackle the problem of learning complex, general behaviors directly in the real world.
Curious model-building control systems
Jürgen Schmidhuber · 1991
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The cross-entropy method for combinatorial and continuous optimization
Reuven Rubinstein · 1999
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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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Reinforcement learning for humanoid robotics
Jan Peters, Sethu Vijayakumar, and Stefan Schaal · 2003
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Reinforcement learning by reward-weighted regression for operational space control
Jan Peters and Stefan Schaal · 2007
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Learning motor primitives for robotics
Jens Kober and Jan Peters · 2009
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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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Pilco: A model-based and data-efficient approach to policy search
Marc Deisenroth and Carl E Rasmussen · 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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A survey on policy search for robotics
Marc Peter Deisenroth, Gerhard Neumann, and Jan Peters · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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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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Hierarchical relative entropy policy search
Christian Daniel, Gerhard Neumann, Oliver Kroemer, and Jan Peters · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 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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The option-critic architecture
Pierre-Luc Bacon, Jean Harb, and Doina Precup · 2017
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, and Sergey Levine · 2017
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Unsupervised perceptual rewards for imitation learning
Pierre Sermanet, Kelvin Xu, and Sergey Levine · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Imagination-augmented agents for deep reinforcement learning
Théophane Weber, Sébastien Racanière, David P Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, et al · 2017
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
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Scaling egocentric vision: The epic-kitchens dataset
Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray · 2018
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Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine · 2018
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David Ha and Jürgen Schmidhuber · 2018
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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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Stochastic adversarial video prediction
Alex X Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, and Sergey Levine · 2018
Cited alongside, same era.
Domain randomization and generative models for robotic grasping
Josh Tobin, Lukas Biewald, Rocky Duan, Marcin Andrychowicz, Ankur Handa, Vikash Kumar, Bob McGrew, Alex Ray, Jonas Schneider, Peter Welinder, et al · 2018
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Learning synergies between pushing and grasping with self-supervised deep reinforcement learning
Bc-z: Zero-shot task generalization with robotic imitation learning
Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, and Chelsea Finn · 2021
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Mt-opt: Continuous multi-task robotic reinforcement learning at scale
Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, and Karol Hausman · 2021
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Rma: Rapid motor adaptation for legged robots
Ashish Kumar, Zipeng Fu, Deepak Pathak, and Jitendra Malik · 2021
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Polymetis
Yixin Lin, Austin S. Wang, Giovanni Sutanto, Akshara Rai, and Franziska Meier · 2021
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Discovering and achieving goals via world models
Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, and Deepak Pathak · 2021
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Andy Zeng, Shuran Song, Stefan Welker, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 2018
Cited alongside, same era.
Robonet: Large-scale multi-robot learning
Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, and Chelsea Finn · 2019
Cited alongside, same era.
Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2019
Cited alongside, same era.
Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model
Alex X Lee, Anusha Nagabandi, Pieter Abbeel, and Sergey Levine · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Grounded human-object interaction hotspots from video
Tushar Nagarajan, Christoph Feichtenhofer, and Kristen Grauman · 2019
Cited alongside, same era.
Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2019
Cited alongside, same era.
Third-person visual imitation learning via decoupled hierarchical controller
Pratyusha Sharma, Deepak Pathak, and Abhinav Gupta · 2019
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Concept2robot: Learning manipulation concepts from instructions and human demonstrations
Lin Shao, Toki Migimatsu, Qiang Zhang, Karen Yang, and Jeannette Bohg · 2021
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Example-driven model-based reinforcement learning for solving long-horizon visuomotor tasks
Bohan Wu, Suraj Nair, Li Fei-Fei, and Chelsea Finn · 2021
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Learning by watching: Physical imitation of manipulation skills from human videos
Haoyu Xiong, Quanzhou Li, Yun-Chun Chen, Homanga Bharadhwaj, Samarth Sinha, and Animesh Garg · 2021
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Xirl: Cross-embodiment inverse reinforcement learning
Kevin Zakka, Andy Zeng, Pete Florence, Jonathan Tompson, Jeannette Bohg, and Debidatta Dwibedi · 2021
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Dexterous imitation made easy: A learning-based framework for efficient dexterous manipulation
Sridhar Pandian Arunachalam, Sneha Silwal, Ben Evans, and Lerrel Pinto · 2022
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Human-to-robot imitation in the wild
Shikhar Bahl, Abhinav Gupta, and Deepak Pathak · 2022
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Rt-1: Robotics transformer for real-world control at scale
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, et al · 2022
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Human hands as probes for interactive object understanding
Mohit Goyal, Sahil Modi, Rishabh Goyal, and Saurabh Gupta · 2022
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Modem: Accelerating visual model-based reinforcement learning with demonstrations
Nicklas Hansen, Yixin Lin, Hao Su, Xiaolong Wang, Vikash Kumar, and Aravind Rajeswaran · 2022
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Coarse-to-fine q-attention: Efficient learning for visual robotic manipulation via discretisation
Stephen James, Kentaro Wada, Tristan Laidlow, and Andrew J Davison · 2022
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Joint hand motion and interaction hotspots prediction from egocentric videos
Shaowei Liu, Subarna Tripathi, Somdeb Majumdar, and Xiaolong Wang · 2022
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Dexvip: Learning dexterous grasping with human hand pose priors from video
Priyanka Mandikal and Kristen Grauman · 2022
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Dexmv: Imitation learning for dexterous manipulation from human videos
Yuzhe Qin, Yueh-Hua Wu, Shaowei Liu, Hanwen Jiang, Ruihan Yang, Yang Fu, and Xiaolong Wang · 2022
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Real-world robot learning with masked visual pre-training
Ilija Radosavovic, Tete Xiao, Stephen James, Pieter Abbeel, Jitendra Malik, and Trevor Darrell · 2022
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Videodex: Learning dexterity from internet videos
Kenneth Shaw, Shikhar Bahl, and Deepak Pathak · 2022
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Daydreamer: World models for physical robot learning
Philipp Wu, Alejandro Escontrela, Danijar Hafner, Ken Goldberg, and Pieter Abbeel · 2022
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Masked visual pre-training for motor control
Tete Xiao, Ilija Radosavovic, Trevor Darrell, and Jitendra Malik · 2022
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Detecting twenty-thousand classes using image-level supervision
Xingyi Zhou, Rohit Girdhar, Armand Joulin, Phillip Krähenbühl, and Ishan Misra · 2022
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Affordances from human videos as a versatile representation for robotics
Shikhar Bahl, Russell Mendonca, Lili Chen, Unnat Jain, and Deepak Pathak · 2023
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Alan: Autonomously exploring robotic agents in the real world
Russell Mendonca, Shikhar Bahl, and Deepak Pathak · 2023
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