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2020

Learning to Play Table Tennis From Scratch using Muscular Robots

Büchler, Dieter, Guist, Simon, Calandra, Roberto et al.

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Dynamic tasks like table tennis are relatively easy to learn for humans but pose significant challenges to robots.

Such tasks require accurate control of fast movements and precise timing in the presence of imprecise state estimation of the flying ball and the robot.
Reinforcement Learning (RL) has shown promise in learning of complex control tasks from data.
However, applying step-based RL to dynamic tasks on real systems is safety-critical as RL requires exploring and failing safely for millions of time steps in high-speed regimes.

Learning to Play Table Tennis From Scratch using Muscular Robots · Around

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D. Schwab, T. Springenberg, M. F. Martins, T. Lampe, M. Neunert, A. Abdolmaleki, T. Hertweck, R. Hafner, F. Nori, and M. Riedmiller, “Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup,” arXiv:1902.04706 [cs, stat] , Feb. 2019, arXiv: 1902.04706. [Online]. Available: http://arxiv.org/abs/1902.04706
Original
1902
Earlier work this paper cites.
G. Dulac-Arnold, D. Mankowitz, and T. Hester, “Challenges of Real-World Reinforcement Learning,” arXiv:1904.12901 [cs, stat] , Apr. 2019, arXiv: 1904.12901. [Online]. Available: http://arxiv.org/abs/1904.12901
Original
1904
Earlier work this paper cites.
D. Büchler, R. Calandra, and J. Peters, “Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots,” arXiv:1904.03665 [cs] , Apr. 2019, arXiv: 1904.03665. [Online]. Available: http://arxiv.org/abs/1904.03665
Original
1904
Earlier work this paper cites.
S. Gomez-Gonzalez, Y. Nemmour, B. Schölkopf, and J. Peters, “Reliable Real Time Ball Tracking for Robot Table Tennis,” arXiv:1908.07332 [cs] , Aug. 2019, arXiv: 1908.07332. [Online]. Available: http://arxiv.org/abs/1908.07332
Original
1908
Earlier work this paper cites.
C. Bodnar, A. Li, K. Hausman, P. Pastor, and M. Kalakrishnan, “Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping,” arXiv:1910.02787 [cs, stat] , Oct. 2019, arXiv: 1910.02787. [Online]. Available: http://arxiv.org/abs/1910.02787
Original
1910
Earlier work this paper cites.
Ramanantsoa and Durey, “Towards a stroke Construction Model,” ITTF Education , Jan. 1994. [Online]. Available: https://www.ittfeducation.com/towards-stroke-construction-model/
1994
Earlier work this paper cites.
T. Senoo, A. Namiki, and M. Ishikawa, “Ball control in high-speed batting motion using hybrid trajectory generator,” in Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006. , May 2006, pp. 1762–1767
2006
Earlier work this paper cites.
K. Muelling, J. Kober, and J. Peters, “Learning table tennis with a Mixture of Motor Primitives,” in 2010 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids) , Dec. 2010, pp. 411–416
2010
Earlier work this paper cites.
J. Kober, K. Mülling, O. Krömer, C. H. Lampert, B. Schölkopf, and J. Peters, “Movement templates for learning of hitting and batting,” in 2010 IEEE International Conference on Robotics and Automation , May 2010, pp. 853–858
2010
Earlier work this paper cites.
K. Mülling, J. Kober, and J. Peters, “A biomimetic approach to robot table tennis,” Adaptive Behavior , vol. 19, no. 5, pp. 359–376, Oct. 2011. [Online]. Available: http://adb.sagepub.com/content/19/5/359
2011
Earlier work this paper cites.

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2013

Cited alongside, same era.

G. Neumann, C. Daniel, A. Paraschos, A. Kupcsik, and J. Peters, “Learning Modular Policies for Robotics,” Frontiers in Computational Neuroscience , vol. 8, p. 62, 2014. [Online]. Available: http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00062/pdf

2014

Cited alongside, same era.

K. Muelling, A. Boularias, B. Mohler, B. Schölkopf, and J. Peters, “Learning strategies in table tennis using inverse reinforcement learning.” Biological cybernetics , 2014. [Online]. Available: http://www.ias.tu-darmstadt.de/uploads/Site/EditPublication/Muelling_BICY_2014.pdf

2014

Cited alongside, same era.

S. Gu, E. Holly, T. Lillicrap, and S. Levine, “Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates,” arXiv:1610.00633 [cs] , Oct. 2016, arXiv: 1610.00633. [Online]. Available: http://arxiv.org/abs/1610.00633

2016

Cited alongside, same era.

Y. Huang, D. Büchler, O. Koç, B. Schölkopf, and J. Peters, “Jointly learning trajectory generation and hitting point prediction in robot table tennis,” in 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids) , Nov. 2016, pp. 650–655

2016

Cited alongside, same era.

D. Büchler, H. Ott, and J. Peters, “A Lightweight Robotic Arm with Pneumatic Muscles for Robot Learning,” in International Conference on Robotics and Automation (ICRA) , Stockholm, May 2016

2016

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S. Kawakami, M. Ikumo, and T. Oya, “Omron table tennis robot forpheus,” Tech. Rep., 2016. [Online]. Available: https://www.omron.com/innovation/forpheus.html

2016

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2017

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P. Dhariwal, C. Hesse, O. Klimov, A. Nichol, M. Plappert, A. Radford, J. Schulman, S. Sidor, and Y. Wu, “Openai baselines,” GitHub, GitHub repository , 2017

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OpenAI, 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, “Learning Dexterous In-Hand Manipulation,” arXiv:1808.00177 [cs, stat] , Aug. 2018, arXiv: 1808.00177. [Online]. Available: http://arxiv.org/abs/1808.00177

2018

Later among the works it cites.

S. Gomez-Gonzalez, G. Neumann, B. Schölkopf, and J. Peters, “Adaptation and Robust Learning of Probabilistic Movement Primitives,” arXiv:1808.10648 [cs, stat] , Aug. 2018, arXiv: 1808.10648. [Online]. Available: http://arxiv.org/abs/1808.10648

2018

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O. Koç, G. Maeda, and J. Peters, “Online optimal trajectory generation for robot table tennis,” Robotics and Autonomous Systems , vol. 105, pp. 121–137, Jul. 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0921889017306164

2018

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D. Büchler, R. Calandra, B. Schölkopf, and J. Peters, “Control of Musculoskeletal Systems using Learned Dynamics Models,” IEEE Robotics and Automation Letters , 2018

2018

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O. Koç, G. Maeda, and J. Peters, “Optimizing the Execution of Dynamic Robot Movements With Learning Control,” IEEE Transactions on Robotics , vol. 35, no. 4, pp. 909–924, Aug. 2019

2019

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