HITTER: A HumanoId Table TEnnis Robot via Hierarchical Planning and Learning

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Z. Wang, A. Boularias, K. Mülling, and J. Peters, “Modeling opponent actions for table-tennis playing robot,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 25, no. 1, pp. 1828–1829, Aug. 2011. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/8051

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Z. Wang, C. H. Lampert, K. Mülling, B. Schölkopf, and J. Peters, “Learning anticipation policies for robot table tennis,” in 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems , 2011, pp. 332–337

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R. Xiong, Y. Sun, Q. Zhu, J. Wu, and J. Chu, “Impedance control and its effects on a humanoid robot playing table tennis,” International Journal of Advanced Robotic Systems , vol. 9, no. 5, p. 178, 2012

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K. Mülling, J. Kober, O. Kroemer, and J. Peters, “Learning to select and generalize striking movements in robot table tennis,” The International Journal of Robotics Research , vol. 32, pp. 263–279, 03 2013

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Z. Wang, K. Muelling, M. P. Deisenroth, H. Ben Amor, D. Vogt, B. Schoelkopf, and J. Peters, “Probabilistic movement modeling for intention inference in human-robot interaction,” International Journal of Robotics Research , vol. 32, no. 7, pp. 841 – 858, June 2013

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K. Mülling, A. Boularias, B. Mohler, B. Schölkopf, and J. Peters, “Learning strategies in table tennis using inverse reinforcement learning,” Biological Cybernetics , vol. 108, no. 5, pp. 603–619, 2014

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Z. Wang, A. Boularias, K. Mülling, B. Schölkopf, and J. Peters, “Anticipatory action selection for human–robot table tennis,” Artificial Intelligence , vol. 247, pp. 399–414, 2017, special Issue on AI and Robotics. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0004370214001398

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J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017

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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, 2018

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X. B. Peng, P. Abbeel, S. Levine, and M. Van de Panne, “Deepmimic: Example-guided deep reinforcement learning of physics-based character skills,” ACM Transactions On Graphics (TOG) , vol. 37, no. 4, pp. 1–14, 2018

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L. Pinto, M. Andrychowicz, P. Welinder, W. Zaremba, and P. Abbeel, “Asymmetric actor critic for image-based robot learning,” in 14th Robotics: Science and Systems, RSS 2018 . MIT Press Journals, 2018

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Y. Ji, X. Hu, Y. Chen, Y. Mao, G. Wang, Q. Li, and J. Zhang, “Model-based trajectory prediction and hitting velocity control for a new table tennis robot,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 2728–2734

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J. Tebbe, L. Krauch, Y. Gao, and A. Zell, “Sample-efficient reinforcement learning in robotic table tennis,” in 2021 IEEE international conference on robotics and automation (ICRA) . IEEE, 2021, pp. 4171–4178

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Z. Zaidi, D. Martin, N. Belles, V. Zakharov, A. Krishna, K. M. Lee, P. Wagstaff, S. Naik, M. Sklar, S. Choi, et al. , “Athletic mobile manipulator system for robotic wheelchair tennis,” IEEE Robotics and Automation Letters , vol. 8, no. 4, pp. 2245–2252, 2023

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D. B. D’Ambrosio, N. Jaitly, V. Sindhwani, K. Oslund, P. Xu, N. Lazic, A. Shankar, T. Ding, J. Abelian, E. Coumans, et al. , “Robotic Table Tennis: A Case Study into a High Speed Learning System,” in Proceedings of Robotics: Science and Systems , Daegu, Republic of Korea, July 2023

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2025

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Y. Ma, A. Cramariuc, F. Farshidian, and M. Hutter, “Learning coordinated badminton skills for legged manipulators,” Science Robotics , vol. 10, no. 102, p. eadu3922, 2025

2025

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D. Etaat, D. Kalaria, N. Rahmanian, and S. S. Sastry, “Latte-mv: Learning to anticipate table tennis hits from monocular videos,” in Proceedings of the Computer Vision and Pattern Recognition Conference , 2025, pp. 7115–7124

2025

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D. Nguyen, K. D. Cancio, and S. Kim, “High speed robotic table tennis swinging using lightweight hardware with model predictive control,” in 2025 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2025

2025

Closest in time.