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Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously.
N. Hogan, “Impedance control: An approach to manipulation,” in
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M.-C. Chien and A.-C. Huang, “Adaptive impedance control of robot manipulators based on function approximation technique,”
2004
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N. Koenig and A. Howard, “Design and use paradigms for gazebo, an open-source multi-robot simulator,” in
2004
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R. Campa and K. Camarillo, “Unit quaternions: A mathematical tool for modeling, path planning and control of robot manipulators,” in
2008
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E. Theodorou, J. Buchli, and S. Schaal, “Reinforcement learning of motor skills in high dimensions: A path integral approach,” in
2010
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D. Mitrovic, S. Klanke, and S. Vijayakumar, “Learning impedance control of antagonistic systems based on stochastic optimization principles,”
2011
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J. Buchli, F. Stulp, E. Theodorou, and S. Schaal, “Learning variable impedance control,”
2011
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B. Siciliano and L. Villani,
2012
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2015
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S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,”
2016
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M. Racca, J. Pajarinen, A. Montebelli, and V. Kyrki, “Learning in-contact control strategies from demonstration,” in
2016
Cited alongside, same era.
L. Pinto and A. Gupta, “Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,” in
2016
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S. Gu, E. Holly, T. Lillicrap, and S. Levine, “Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,” in
2017
Cited alongside, same era.
R. S. Sutton and A. G. Barto,
2018
Later among the works it cites.
2018
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2019
Later among the works it cites.
2019
Later among the works it cites.
G. Schoettler, A. Nair, J. Luo, S. Bahl, J. A. Ojea, E. Solowjow, and S. Levine, “Deep reinforcement learning for industrial insertion tasks with visual inputs and natural rewards,” in
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K. Lynch and F. Park,
2017
Cited alongside, same era.
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, and S. Levine, “Scalable deep reinforcement learning for vision-based robotic manipulation,” in
2018
Cited alongside, same era.
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection,”
2018
Cited alongside, same era.
G. Thomas, M. Chien, A. Tamar, J. A. Ojea, and P. Abbeel, “Learning robotic assembly from cad,” in
2018
Cited alongside, same era.
A. R. Mahmood, D. Korenkevych, G. Vasan, W. Ma, and J. Bergstra, “Benchmarking reinforcement learning algorithms on real-world robots,” in
2018
Cited alongside, same era.
2019
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L. Johannsmeier, M. Gerchow, and S. Haddadin, “A framework for robot manipulation: Skill formalism, meta learning and adaptive control,” in
2019
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2019
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R. Martín-Martín, M. Lee, R. Gardner, S. Savarese, J. Bohg, and A. Garg, “Variable impedance control in end-effector space. an action space for reinforcement learning in contact rich tasks,” in
2019
Later among the works it cites.
J. Luo, E. Solowjow, C. Wen, J. A. Ojea, and A. M. Agogino, “Deep reinforcement learning for robotic assembly of mixed deformable and rigid objects,” in
2069
Closest in time.