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Deep reinforcement learning has recently seen huge success across multiple areas in the robotics domain.
Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1989
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System identification and control using genetic algorithms
Kristinn Kristinsson and Guy Albert Dumont · 1992
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Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart J Russell, et al · 2000
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Design and use paradigms for gazebo, an open-source multi-robot simulator
Nathan Koenig and Andrew Howard · 2004
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Robust reinforcement learning
Jun Morimoto and Kenji Doya · 2005
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Learning force control policies for compliant manipulation
M. Kalakrishnan et al · 2011
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Reinforcement learning in robotics: A survey
J. Kober et al · 2013
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Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
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A comprehensive survey on safe reinforcement learning
Javier Garcıa and Fernando Fernández · 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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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
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Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2015
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Simultaneous deep transfer across domains and tasks
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko · 2015
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Rotors—a modular gazebo mav simulator framework
Fadri Furrer, Michael Burri, Markus Achtelik, and Roland Siegwart · 2016
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Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Domain separation networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan · 2016
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Pybullet, a python module for physics simulation for games, robotics and machine learning
Erwin Coumans and Yunfei Bai · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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A brief survey of deep reinforcement learning
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath · 2017
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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 · 2017
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Carla: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
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Sim-to-real transfer of accurate grasping with eye-in-hand observations and continuous control
Mengyuan Yan, Iuri Frosio, Stephen Tyree, and Jan Kautz · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
Cited alongside, same era.
Learning invariant feature spaces to transfer skills with reinforcement learning
Abhishek Gupta, Coline Devin, YuXuan Liu, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Darla: Improving zero-shot transfer in reinforcement learning
Robust reinforcement learning for continuous control with model misspecification
Daniel J Mankowitz, Nir Levine, Rae Jeong, Yuanyuan Shi, Jackie Kay, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy Mann, Todd Hester, and Martin Riedmiller · 2019
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Bharathan Balaji, Sunil Mallya, Sahika Genc, Saurabh Gupta, Leo Dirac, Vineet Khare, Gourav Roy, Tao Sun, Yunzhe Tao, Brian Townsend, et al · 2019
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Sim-to-real transfer reinforcement learning for control of thermal effects of an atmospheric pressure plasma jet
Matthew Witman, Dogan Gidon, David B Graves, Berend Smit, and Ali Mesbah · 2019
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Modelling generalized forces with reinforcement learning for sim-to-real transfer
Rae Jeong, Jackie Kay, Francesco Romano, Thomas Lampe, Tom Rothorl, Abbas Abdolmaleki, Tom Erez, Yuval Tassa, and Francesco Nori · 2019
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Irina Higgins, Arka Pal, Andrei A Rusu, Loic Matthey, Christopher P Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, and Alexander Lerchner · 2017
Cited alongside, same era.
Sim-to-real robot learning from pixels with progressive nets
Andrei A Rusu, Matej Večerík, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, and Raia Hadsell · 2017
Cited alongside, same era.
Unsupervised pixel-level domain adaptation with generative adversarial networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan · 2017
Cited alongside, same era.
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J Lim, Abhinav Gupta, Li Fei-Fei, and Ali Farhadi · 2017
Cited alongside, same era.
Sim-to-real reinforcement learning for deformable object manipulation
Jan Matas, Stephen James, and Andrew J Davison · 2018
Cited alongside, same era.
Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal Mian · 2018
Cited alongside, same era.
Airsim: High-fidelity visual and physical simulation for autonomous vehicles
Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor · 2018
Cited alongside, same era.
Flexible robotic grasping with sim-to-real transfer based reinforcement learning
Michel Breyer, Fadri Furrer, Tonci Novkovic, Roland Siegwart, and Juan Nieto · 2018
Cited alongside, same era.
Sim-to-real: Six-legged robot control with deep reinforcement learning and curriculum learning
Bangyu Qin, Yue Gao, and Yi Bai · 2019
Later among the works it cites.
Sim-to-real in reinforcement learning for everyone
Juliano Vacaro, Guilherme Marques, Bruna Oliveira, Gabriel Paz, Thomas Paula, Wagston Staehler, and David Murphy · 2019
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” good robot!”: Efficient reinforcement learning for multi-step visual tasks via reward shaping
Andrew Hundt, Benjamin Killeen, Heeyeon Kwon, Chris Paxton, and Gregory D Hager · 2019
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Sim-to-real transfer of robotic gripper pose estimation-using deep reinforcement learning, generative adversarial networks, and visual servoing
Ole-Magnus Pedersen · 2019
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Multi-agent manipulation via locomotion using hierarchical sim2real
Ofir Nachum, Michael Ahn, Hugo Ponte, Shixiang Gu, and Vikash Kumar · 2019
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Real-World Robotic Perception and Control Using Synthetic Data
Joshua P Tobin · 2019
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Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data
Xiangyu Yue, Yang Zhang, Sicheng Zhao, Alberto Sangiovanni-Vincentelli, Kurt Keutzer, and Boqing Gong · 2019
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Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, and Konstantinos Bousmalis · 2019
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Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications
Thanh Thi Nguyen, Ngoc Duy Nguyen, and Saeid Nahavandi · 2020
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Towards closing the sim-to-real gap in collaborative multi-robot deep reinforcement learning
Wenshuai Zhao, Jorge Peña Queralta, Li Qingqing, and Tomi Westerlund · 2020
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Bayesian domain randomization for sim-to-real transfer
Fabio Muratore, Christian Eilers, Michael Gienger, and Jan Peters · 2020
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Blind spot detection for safe sim-to-real transfer
Ramya Ramakrishnan, Ece Kamar, Debadeepta Dey, Eric Horvitz, and Julie Shah · 2020
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A comprehensive survey on transfer learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2020
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Collaborative multi-robot systems for search and rescue: Coordination and perception
Jorge Peña Queralta, Jussi Taipalmaa, Bilge Can Pullinen, Victor Kathan Sarker, Tuan Nguyen Gia, Hannu Tenhunen, Moncef Gabbouj, Jenni Raitoharju, and Tomi Westerlund · 2020
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Sim2real transfer for reinforcement learning without dynamics randomization
Manuel Kaspar, Juan David Munoz Osorio, and Jürgen Bock · 2020
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Learning to play soccer by reinforcement and applying sim-to-real to compete in the real world
Hansenclever F Bassani, Renie A Delgado, Jose Nilton de O Lima Junior, Heitor R Medeiros, Pedro HM Braga, and Alain Tapp · 2020
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Thomas Chaffre, Julien Moras, Adrien Chan-Hon-Tong, and Julien Marzat · 2020
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Sim-to-real transfer for optical tactile sensing
Zihan Ding, Nathan F Lepora, and Edward Johns · 2020
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Learning dexterous in-hand manipulation
OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, et al · 2020
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Reinforcement learning with perturbed rewards
Jingkang Wang, Yang Liu, and Bo Li · 2020
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Ubiquitous distributed deep reinforcement learning at the edge: Analyzing byzantine agents in discrete action spaces
Wenshuai Zhao, Jorge Peña Queralta, Li Qingqing, and Tomi Westerlund · 2020
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A survey on visual navigation for artificial agents with deep reinforcement learning
Fanyu Zeng, Chen Wang, and Shuzhi Sam Ge · 2020
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