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Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL).
Quan Vuong, Sharad Vikram, Hao Su, Sicun Gao, and Henrik I Christensen · 1903
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Quan Vuong, Sharad Vikram, Hao Su, Sicun Gao, and Henrik I Christensen · 1903
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Intrinsically motivated goal exploration for active motor learning in robots: A case study
Adrien Baranes and Pierre-Yves Oudeyer · 2010
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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
Jens Kober, J Andrew Bagnell, and Jan Peters · 2013
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Reinforcement learning for pivoting task
Rika Antonova, Silvia Cruciani, Christian Smith, and Danica Kragic · 2017
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Reverse curriculum generation for reinforcement learning
Carlos Florensa, David Held, Markus Wulfmeier, Michael Zhang, and Pieter Abbeel · 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
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Automatic goal generation for reinforcement learning agents
Carlos Florensa, David Held, Xinyang Geng, and Pieter Abbeel · 2018
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Accuracy-based curriculum learning in deep reinforcement learning
Pierre Fournier, Olivier Sigaud, Mohamed Chetouani, and Pierre-Yves Oudeyer · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Markov decision processes with continuous side information
Aditya Modi, Nan Jiang, Satinder Singh, and Ambuj Tewari · 2018
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Sim-to-real transfer of robotic control with dynamics randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Sim-to-real: Learning agile locomotion for quadruped robots
Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, and Vincent Vanhoucke · 2018
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Policy transfer with strategy optimization
Wenhao Yu, C Karen Liu, and Greg Turk · 2018
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Sim-to-real transfer in deep reinforcement learning for robotics: a survey
Wenshuai Zhao, Jorge Peña Queralta, and Tomi Westerlund · 2020
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Sim-to-real transfer for robotic manipulation with tactile sensory
Zihan Ding, Ya-Yen Tsai, Wang Wei Lee, and Bidan Huang · 2021
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DROID: Minimizing the Reality Gap Using Single-Shot Human Demonstration
Ya-Yen Tsai, Hui Xu, Zihan Ding, Chong Zhang, Edward Johns, and Bidan Huang · 2021
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Understanding domain randomization for sim-to-real transfer
Xiaoyu Chen, Jiachen Hu, Chi Jin, Lihong Li, and Liwei Wang · 2022
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Curriculum reinforcement learning using optimal transport via gradual domain adaptation
Peide Huang, Mengdi Xu, Jiacheng Zhu, Laixi Shi, Fei Fang, and Ding Zhao · 2022
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Fast model identification via physics engines for data-efficient policy search
Shaojun Zhu, Andrew Kimmel, Kostas E Bekris, and Abdeslam Boularias · 2018
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Solving rubik’s cube with a robot hand
Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al · 2019
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Closing the sim-to-real loop: Adapting simulation randomization with real world experience
Yevgen Chebotar, Ankur Handa, Viktor Makoviychuk, Miles Macklin, Jan Issac, Nathan Ratliff, and Dieter Fox · 2019
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Assessing transferability from simulation to reality for reinforcement learning
Fabio Muratore, Michael Gienger, and Jan Peters · 2019
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Active domain randomization
Bhairav Mehta, Manfred Diaz, Florian Golemo, Christopher J. Pal, and Liam Paull · 2020
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Learning domain randomization distributions for training robust locomotion policies
Melissa Mozian, Juan Camilo Gamboa Higuera, David Meger, and Gregory Dudek · 2020
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Crossing the gap: A deep dive into zero-shot sim-to-real transfer for dynamics
Eugene Valassakis, Zihan Ding, and Edward Johns · 2020
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Josip Josifovski, Mohammadhossein Malmir, Noah Klarmann, Bare Luka Žagar, Nicolás Navarro-Guerrero, and Alois Knoll · 2022
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Curriculum reinforcement learning via constrained optimal transport
Pascal Klink, Haoyi Yang, Carlo D’Eramo, Jan Peters, and Joni Pajarinen · 2022
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Online vs. offline adaptive domain randomization benchmark
Gabriele Tiboni, Karol Arndt, Giuseppe Averta, Ville Kyrki, and Tatiana Tommasi · 2022
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Daesol Cho, Seungjae Lee, and H Jin Kim · 2023
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Reward-machine-guided, self-paced reinforcement learning
Cevahir Koprulu and Ufuk Topcu · 2023
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Dropo: Sim-to-real transfer with offline domain randomization
Gabriele Tiboni, Karol Arndt, and Ville Kyrki · 2023
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