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We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot.
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M. A. Erdmann and M. T. Mason · 1988
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N. Sawasaki and H. INOUE · 1991
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D. Rus · 1992
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Y. Aiyama, M. Inaba, and H. Inoue · 1993
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Stably supported rotations of a planar polygon with two frictionless contacts
T. Abell and M. A. Erdmann · 1995
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A. Bicchi and R. Sorrentino · 1995
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L. Han, Y. Guan, Z. X. Li, S. Qi, and J. C. Trinkle · 1997
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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An exploration of nonprehensile two-palm manipulation
M. A. Erdmann · 1998
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Dextrous manipulation by rolling and finger gaiting
L. Han and J. C. Trinkle · 1998
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Planning quasi-static fingertip manipulations for reconfiguring objects
M. Cherif and K. K. Gupta · 1999
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In-hand dexterous manipulation of piecewise-smooth 3-d objects
D. Rus · 1999
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Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity
A. Bicchi · 2000
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Mechanics, planning, and control for tapping
W. H. Huang and M. T. Mason · 2000
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An overview of dexterous manipulation
A. M. Okamura, N. Smaby, and M. R. Cutkosky · 2000
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The intelligent asimo: System overview and integration
Y. Sakagami, R. Watanabe, C. Aoyama, S. Matsunaga, N. Higaki, and K. Fujimura · 2002
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ShadowRobot Dexterous Hand
ShadowRobot · 2005
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Visualizing data using t-SNE
L. van der Maaten and G. Hinton · 2008
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Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
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No-regret reductions for imitation learning and structured prediction
S. Ross, G. J. Gordon, and J. A. Bagnell · 2010
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Dynamic object manipulation using a virtual frame by a triple soft-fingered robotic hand
K. Tahara, S. Arimoto, and M. Yoshida · 2010
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On dexterity and dexterous manipulation
R. R. Ma and A. M. Dollar · 2011
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J. Schmidhuber · 2011
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Contact-invariant optimization for hand manipulation
I. Mordatch, Z. Popovic, and E. Todorov · 2012
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First experiments with powerplay
R. K. Srivastava, B. R. Steunebrink, and J. Schmidhuber · 2012
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MuJoCo: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
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Armar-4: A 63 dof torque controlled humanoid robot
T. Asfour, J. Schill, H. Peters, C. Klas, J. Bücker, C. Sander, S. Schulz, A. Kargov, T. Werner, and V. Bartenbach · 2013
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On rolling contact motion by robotic fingers via prescribed performance control
Z. Doulgeri and L. Droukas · 2013
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MindCuber
D. Gilday · 2013
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Guided policy search
S. Levine and V. Koltun · 2013
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Rotary object dexterous manipulation in hand: a feedback-based method
Q. Li, M. Meier, R. Haschke, H. J. Ritter, and B. Bolder · 2013
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Dexterous manipulation using both palm and fingers
Y. Bai and C. K. Liu · 2014
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Extrinsic dexterity: In-hand manipulation with external forces
N. C. Dafle, A. Rodriguez, R. Paolini, B. Tang, S. S. Srinivasa, M. A. Erdmann, M. T. Mason, I. Lundberg, H. Staab, and T. A. Fuhlbrigge · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Learning of grasp adaptation through experience and tactile sensing
M. Li, Y. Bekiroglu, D. Kragic, and A. Billard · 2014
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Learning object-level impedance control for robust grasping and dexterous manipulation
M. Li, H. Yin, K. Tahara, and A. Billard · 2014
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God’s Number is 26 in the Quarter-Turn Metric
T. Rokicki and M. Davidson · 2014
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Robots that can adapt like animals
A. Cully, J. Clune, D. Tarapore, and J.-B. Mouret · 2015
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Learning contact-rich manipulation skills with guided policy search
S. Levine, N. Wagener, and P. Abbeel · 2015
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Kociemba
M. Tsoy · 2015
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Towards adapting deep visuomotor representations from simulated to real environments
E. Tzeng, C. Devin, J. Hoffman, C. Finn, X. Peng, S. Levine, K. Saenko, and T. Darrell · 2015
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Learning robot in-hand manipulation with tactile features
H. van Hoof, T. Hermans, G. Neumann, and J. Peters · 2015
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Fastest robot to solve a Rubik’s Cube
A. Beer · 2016
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Experiments in handwriting with a neural network
S. Carter, D. Ha, I. Johnson, and C. Olah · 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
Exploration by random network distillation
Y. Burda, H. Edwards, A. Storkey, and O. Klimov · 2018
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Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience
Y. Chebotar, A. Handa, V. Makoviychuk, M. Macklin, J. Issac, N. Ratliff, and D. Fox · 2018
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Learning to adapt: Meta-learning for model-based control
I. Clavera, A. Nagabandi, R. S. Fearing, P. Abbeel, S. Levine, and C. Finn · 2018
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AutoAugment: Learning Augmentation Policies from Data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2018
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On policy learning robust to irreversible events: An application to robotic in-hand manipulation
P. Falco, A. Attawia, M. Saveriano, and D. Lee · 2018
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RL 2 : Fast reinforcement learning via slow reinforcement learning
Y. Duan, J. Schulman, X. Chen, P. L. Bartlett, I. Sutskever, and P. Abbeel · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Learning dexterous manipulation policies from experience and imitation
V. Kumar, A. Gupta, E. Todorov, and S. Levine · 2016
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Optimal control with learned local models: Application to dexterous manipulation
V. Kumar, E. Todorov, and S. Levine · 2016
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nRF52832 Product Specification v1.1
Nordic Semiconductor · 2016
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Mastering the game of go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis · 2016
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Dynamic task prioritization for multitask learning
M. Guo, A. Haque, D.-A. Huang, S. Yeung, and L. Fei-Fei · 2018
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Unsupervised meta-learning for reinforcement learning
A. Gupta, B. Eysenbach, C. Finn, and S. Levine · 2018
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Soft actor-critic algorithms and applications
T. Haarnoja, A. Zhou, K. Hartikainen, G. Tucker, S. Ha, J. Tan, V. Kumar, H. Zhu, A. Gupta, P. Abbeel, and S. Levine · 2018
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Rubik’s cube handling using a high-speed multi-fingered hand and a high-speed vision system
R. Higo, Y. Yamakawa, T. Senoo, and M. Ishikawa · 2018
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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 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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The building blocks of interpretability
C. Olah, A. Satyanarayan, I. Johnson, S. Carter, L. Schubert, K. Ye, and A. Mordvintsev · 2018
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OpenAI Five
OpenAI · 2018
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Learning dexterous in-hand manipulation
OpenAI, M. Andrychowicz, B. Baker, M. Chociej, R. Józefowicz, 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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Multi-goal reinforcement learning: Challenging robotics environments and request for research
M. Plappert, M. Andrychowicz, A. Ray, B. McGrew, B. Baker, G. Powell, J. Schneider, J. Tobin, M. Chociej, P. Welinder, et al · 2018
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Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data
A. Prakash, S. Boochoon, M. Brophy, D. Acuna, E. Cameracci, G. State, O. Shapira, and S. Birchfield · 2018
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Learning To Simulate
N. Ruiz, S. Schulter, and M. Chandraker · 2018
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Meta reinforcement learning with latent variable gaussian processes
S. Sæmundsson, K. Hofmann, and M. P. Deisenroth · 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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SOLAR: deep structured latent representations for model-based reinforcement learning
M. Zhang, S. Vikram, L. Smith, P. Abbeel, M. J. Johnson, and S. Levine · 2018
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Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost
H. Zhu, A. Gupta, A. Rajeswaran, S. Levine, and V. Kumar · 2018
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Reinforcement and imitation learning for diverse visuomotor skills
Y. Zhu, Z. Wang, J. Merel, A. A. Rusu, T. Erez, S. Cabi, S. Tunyasuvunakool, J. Kramár, R. Hadsell, N. de Freitas, and N. Heess · 2018
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Reinforcement learning, fast and slow
M. Botvinick, S. Ritter, J. X. Wang, Z. Kurth-Nelson, C. Blundell, and D. Hassabis · 2019
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Orrb – openai remote rendering backend
M. Chociej, P. Welinder, and L. Weng · 2019
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S. Christen, S. Stevsic, and O. Hilliges · 2019
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J. Clune · 2019
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Distilling policy distillation
W. Czarnecki, R. Pascanu, S. Osindero, S. M. Jayakumar, G. Swirszcz, and M. Jaderberg · 2019
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Meta reinforcement learning as task inference
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Learning agile and dynamic motor skills for legged robots
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Meta-Sim: Learning to Generate Synthetic Datasets
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Learning to Solve a Rubik’s Cube with a Dexterous Hand
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Learning latent plans from play
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Active Domain Randomization
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