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We have seen much recent progress in rigid object manipulation, but interaction with deformable objects has notably lagged behind.
An image synthesizer
K. Perlin · 1985
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An Application of Reinforcement Learning to Aerobatic Helicopter Flight
P. Abbeel, A. Coates, M. Quigley, and A. Y. Ng · 2006
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Unfolding of Massive Laundry and Classification Types by Dual Manipulator
F. Osawa, H. Seki, and Y. Kamiy · 2007
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Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding
J. Maitin-Shepard, M. Cusumano-Towner, J. Lei, and P. Abbeel · 2010
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Reinforcement Learning of Clothing Assistance with a Dual-arm Robot
T. Tamei, T. Matsubara, A. Rai, and T. Shibata · 2011
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Bringing clothing into desired configurations with limited perception
M. Cusumano-Towner, A. Singh, S. Miller, J. F. O’Brien, and P. Abbeel · 2011
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Motion planning for dynamic folding of a cloth with two high-speed robot hands and two high-speed sliders
Y. Yamakawa, A. Namiki, and M. Ishikawa · 2011
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Bimanual robotic cloth manipulation for laundry folding
C. Bersch, B. Pitzer, and S. Kammel · 2011
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Combining imitation and reinforcement learning to fold deformable planar objects
B. Balaguer and S. Carpin · 2011
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Generalization in Robotic Manipulation Through The Use of Non-Rigid Registration
J. Schulman, J. Ho, C. Lee, and P. Abbeel · 2013
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A Heuristic-Based Approach for Flattening Wrinkled Clothes
K. Sun, G. Aragon-Camarasa, P. Cockshott, S. Rogers, J. P. Siebert, L. Sun, G. Aragon-Camarasa, P. Cockshott, S. Rogers, and J. P. Siebert · 2013
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A Geometric Approach to Robotic Laundry Folding
S. Miller, J. van den Berg, M. Fritz, T. Darrell, K. Goldberg, and P. Abbeel · 2014
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Folding Deformable Objects using Predictive Simulation and Trajectory Optimization
Y. Li, Y. Yue, D. Xu, E. Grinspun, and P. Allen · 2015
Cited alongside, same era.
Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
Cited alongside, same era.
Learning from Multiple Demonstrations using Trajectory-Aware Non-Rigid Registration with Applications to Deformable Object Manipulation
A. X. Lee, A. Gupta, H. Lu, S. Levine, and P. Abbeel · 2015
Cited alongside, same era.
Prioritized Experience Replay
T. Schaul, J. Quan, I. Antonoglou, and D. Silver · 2015
Cited alongside, same era.
Iterative path optimisation for personalised dressing assistance using vision and force information
Y. Gao, H. J. Chang, and Y. Demiris · 2016
Cited alongside, same era.
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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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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DeepLoco: Dynamic Locomotion Skills Using Hierarchical Deep Reinforcement Learning
X. B. Peng, G. Berseth, and Y. Kangkang · 2017
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Hindsight Experience Replay
M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. Mcgrew, J. Tobin, P. Abbeel, and W. Z. Openai · 2017
Later among the works it cites.
Overcoming Exploration in Reinforcement Learning with Demonstrations
A. Nair, B. McGrew, M. Andrychowicz, W. Zaremba, and P. Abbeel · 2017
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Asymmetric Actor Critic for Image-Based Robot Learning
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S. Gu, E. Holly, T. Lillicrap, and S. Levine · 2016
Cited alongside, same era.
3D Simulation for Robot Arm Control with Deep Q-Learning
S. James and E. Johns · 2016
Cited alongside, same era.
PyBullet, a Python module for physics simulation for games, robotics and machine learning
E. Coumans and Y. Bai · 2016
Cited alongside, same era.
OpenAI Gym
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
Cited alongside, same era.
Learning Robust Bed Making using Deep Imitation Learning with DART
M. Laskey, C. Powers, R. Joshi, A. Poursohi, and K. Goldberg · 2017
Cited alongside, same era.
Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning
B. Thananjeyan, A. Garg, S. Krishnan, C. Chen, L. Miller, and K. Goldberg · 2017
Cited alongside, same era.
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
M. Večerík, T. Hester, J. Scholz, F. Wang, O. Pietquin, B. Piot, N. Heess, T. Rothörl, T. Lampe, and M. Riedmiller · 2017
Cited alongside, same era.
L. Pinto, M. Andrychowicz, P. Welinder, W. Zaremba, and P. Abbeel · 2017
Later among the works it cites.
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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Rainbow: Combining improvements in deep reinforcement learning
M. Hessel, J. Modayil, H. van Hasselt, T. Schaul, G. Ostrovski, W. Dabney, D. Horgan, B. Piot, M. G. Azar, and D. Silver · 2017
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Openai baselines
P. Dhariwal, C. Hesse, O. Klimov, A. Nichol, M. Plappert, A. Radford, J. Schulman, S. Sidor, and Y. Wu · 2017
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Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods
D. Quillen, E. Jang, O. Nachum, C. Finn, J. Ibarz, and S. Levine · 2018
Closest in time.
Addressing Function Approximation Error in Actor-Critic Methods
S. Fujimoto, H. van Hoof, and D. Meger · 2018
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Distributed Distributional Deterministic Policy Gradients
G. Barth-Maro, M. W. Hoffma, D. Budden, W. Dabney, D. Horgan, D. Tb, A. Muldal, N. Heess, T. Lillicrap, and London · 2018
Closest in time.