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We consider the problem of transferring policies to the real world by training on a distribution of simulated scenarios.
Noise and the reality gap: The use of simulation in evolutionary robotics
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Relative entropy policy search
J. Peters, K. Mülling, and Y. Altun · 2010
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M. P. Deisenroth and C. E. Rasmussen · 2011
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M. P. Deisenroth, C. E. Rasmussen, and D. Fox · 2011
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M. P. Deisenroth, G. Neumann, and J. Peters · 2013
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Humanoid robots learning to walk faster: From the real world to simulation and back
A. Farchy, S. Barrett, P. MacAlpine, and P. Stone · 2013
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Generative adversarial nets
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Ensemble-cio: Full-body dynamic motion planning that transfers to physical humanoids
I. Mordatch, K. Lowrey, and E. Todorov · 2015
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
L. Pinto and A. Gupta · 2016
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Transfer from simulation to real world through learning deep inverse dynamics model
P. F. Christiano, Z. Shah, I. Mordatch, J. Schneider, T. Blackwell, J. Tobin, P. Abbeel, and W. Zaremba · 2016
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J. Tan, Z. Xie, B. Boots, and C. K. Liu · 2016
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Epopt: Learning robust neural network policies using model ensembles
A. Rajeswaran, S. Ghotra, S. Levine, and B. Ravindran · 2016
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Generative adversarial imitation learning
J. Ho and S. Ermon · 2016
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Asymmetric actor critic for image-based robot learning
L. Pinto, M. Andrychowicz, P. Welinder, W. Zaremba, and P. Abbeel · 2017
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Grounded action transformation for robot learning in simulation
J. Hanna and P. Stone · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Multi-modal imitation learning from unstructured demonstrations using generative adversarial nets
K. Hausman, Y. Chebotar, S. Schaal, G. S. Sukhatme, and J. J. Lim · 2017
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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen · 2018
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Collective robot reinforcement learning with distributed asynchronous guided policy search
A. Yahya, A. Li, M. Kalakrishnan, Y. Chebotar, and S. Levine · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
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Cad2rl: Real single-image flight without a single real image
F. Sadeghi and S. Levine · 2017
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Transferring end-to-end visuomotor control from simulation to real world for a multi-stage task
S. James, A. J. Davison, and E. Johns · 2017
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Using simulation and domain adaptation to improve efficiency of deep robotic grasping
K. Bousmalis, A. Irpan, P. Wohlhart, Y. Bai, M. Kelcey, M. Kalakrishnan, L. Downs, J. Ibarz, P. Pastor, K. Konolige, S. Levine, and V. Vanhoucke · 2017
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Sim-to-real robot learning from pixels with progressive nets
A. A. Rusu, M. Vecerik, T. Rothörl, N. Heess, R. Pascanu, and R. Hadsell · 2017
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Preparing for the unknown: Learning a universal policy with online system identification
W. Yu, J. Tan, C. K. Liu, and G. Turk · 2017
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, and S. Levine · 2018
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Learning dexterous in-hand manipulation
M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, J. Schneider, S. Sidor, J. Tobin, P. Welinder, L. Weng, and W. Zaremba · 2018
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Sim-to-real transfer of robotic control with dynamics randomization
X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel · 2018
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Domain randomization for simulation-based policy optimization with transferability assessment
F. Muratore, F. Treede, M. Gienger, and J. Peters · 2018
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Sim-to-real: Learning agile locomotion for quadruped robots
J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke · 2018
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Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system
K. Lowrey, S. Kolev, J. Dao, A. Rajeswaran, and E. Todorov · 2018
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Fast model identification via physics engines for data-efficient policy search
S. Zhu, A. Kimmel, K. E. Bekris, and A. Boularias · 2018
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Gpu-accelerated robotic simulation for distributed reinforcement learning
J. Liang, V. Makoviychuk, A. Handa, N. Chentanez, M. Macklin, and D. Fox · 2018
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Time-contrastive networks: Self-supervised learning from video
P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, and S. Levine · 2018
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