Fetching the paper…
Reading the bibliography…
Achieving human-level speed and performance on real world tasks is a north star for the robotics research community.
1910
Earlier work this paper cites.
1912
Earlier work this paper cites.
J. L. Bentley, “Multidimensional binary search trees used for associative searching,” Commun. ACM , vol. 18, no. 9, p. 509–517, sep 1975
1975
Earlier work this paper cites.
J. Billingsley, “Robot ping pong,” Practical Computing , 1983
1983
Earlier work this paper cites.
J. Knight and D. Lowery, “Pingpong-playing robot controlled by a microcomputer,” Microprocessors and Microsystems - Embedded Hardware Design , 1986
1986
Earlier work this paper cites.
R. Brooks, “A robust layered control system for a mobile robot,” IEEE Journal on Robotics and Automation , vol. 2, no. 1, pp. 14–23, 1986
1986
Earlier work this paper cites.
H. Hashimoto, F. Ozaki, and K. Osuka, “Development of a pingpong robot system using 7 degrees of freedom direct drive arm,” in IECON’87: Industrial Applications of Robotics & Machine Vision , vol. 856. SPIE, 1987, pp. 608–615
1987
Earlier work this paper cites.
M. Kawato, K. Furukawa, and R. Suzuki, “A hierarchical neural-network model for control and learning of voluntary movement,” Biological cybernetics , vol. 57, pp. 169–185, 1987
1987
Earlier work this paper cites.
R. L. Andersson, A robot ping-pong player . MIT press Cambridge, Massachusetts, 1988, vol. 988
1988
Earlier work this paper cites.
G. Schweitzer and J. Wen, “Where neural nets make sense in robotics,” in Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic, Volume 1, Volume 2 Prerational Intelligence: Interdisciplinary Perspectives on the Behavior of Natural and Artificial Systems, Volume 3 . Springer, 1994, pp. 530–560
1994
Earlier work this paper cites.
G. Tesauro, “Temporal difference learning and td-gammon,” Commun. ACM , vol. 38, no. 3, p. 58–68, mar 1995
1995
Earlier work this paper cites.
H. Kitano, M. Asada, Y. Kuniyoshi, I. Noda, and E. Osawa, “Robocup: The robot world cup initiative,” in Proceedings of the First International Conference on Autonomous Agents , ser. AGENTS ’97. New York, NY, USA: Association for Computing Machinery, 1997, p. 340–347
1997
Earlier work this paper cites.
J. K. Rosenblatt and C. E. Thorpe, A Behavior-based Architecture for Mobile Navigation . Boston, MA: Springer US, 1997, pp. 19–32
1997
Earlier work this paper cites.
R. C. Arkin, Behavior-Based Robotics . MIT Press, 1998
1998
Earlier work this paper cites.
C. W. Reynolds, “Steering behaviors for autonomous characters,” in Game Developers Conference , 1999, pp. 763–782
1999
Earlier work this paper cites.
B. Triggs, P. F. McLauchlan, R. I. Hartley, and A. W. Fitzgibbon, “Bundle adjustment - a modern synthesis,” in Proceedings of the International Workshop on Vision Algorithms: Theory and Practice , ser. ICCV ’99. London, UK, UK: Springer-Verlag, 2000, pp. 298–372
2000
Earlier work this paper cites.
M. Campbell, A. Hoane, and F. hsiung Hsu, “Deep blue,” Artificial Intelligence , vol. 134, no. 1, pp. 57–83, 2002
2002
Earlier work this paper cites.
M. Matsushima, T. Hashimoto, and F. Miyazaki, “Learning to the robot table tennis task-ball control & rally with a human,” in SMC’03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483) , vol. 3, 2003, pp. 2962–2969 vol.3
2003
Earlier work this paper cites.
P. Stone, R. S. Sutton, and G. Kuhlmann, “Reinforcement learning for robocup soccer keepaway,” Adaptive Behavior , vol. 13, no. 3, pp. 165–188, 2005
2005
Earlier work this paper cites.
S. Behnke, M. Schreiber, J. Stuckler, R. Renner, and H. Strasdat, “See, walk, and kick: Humanoid robots start to play soccer,” in 2006 6th IEEE-RAS International Conference on Humanoid Robots . IEEE, 2006, pp. 497–503
2006
Earlier work this paper cites.
L. van der Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of Machine Learning Research , vol. 9, no. 86, pp. 2579–2605, 2008
2008
Earlier work this paper cites.
K. Muelling, J. Kober, and J. Peters, “ Learning table tennis with a Mixture of Motor Primitives ,” IEEE-RAS Humanoids , 2010
2010
Earlier work this paper cites.
P. Stone, G. Kaminka, S. Kraus, and J. Rosenschein, “Ad hoc autonomous agent teams: Collaboration without pre-coordination,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 24, 2010, pp. 1504–1509
2010
Earlier work this paper cites.
R. S. Sutton, J. Modayil, M. Delp, T. Degris, P. M. Pilarski, A. White, and D. Precup, “Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction.” in AAMAS , L. Sonenberg, P. Stone, K. Tumer, and P. Yolum, Eds., 2011
2011
Earlier work this paper cites.
Y. Sun, R. Xiong, Q. Zhu, J. Wu, and J. Chu, “Balance motion generation for a humanoid robot playing table tennis,” in 2011 11th IEEE-RAS International Conference on Humanoid Robots , 2011, pp. 19–25
2011
Earlier work this paper cites.
A. Nakashima, Y. Ogawa, C. Liu, and Y. Hayakawa, “Robotic table tennis based on physical models of aerodynamics and rebounds,” 2011 IEEE International Conference on Robotics and Biomimetics , 2011
2011
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems , 2012, pp. 5026–5033
2012
Earlier work this paper cites.
T. Petric, L. Peternel, A. Gams, B. Nemec, and L. Zlajpah, “Navigation methods for the skiing robot,” International Journal of Humanoid Robotics , vol. 10, 01 2012
2012
Earlier work this paper cites.
M. Katsikadelis, T. Pilianidis, and N. Mantzouranis, “The interaction between serves and match winning in table tennis players in the london 2012 olympic games,” in Book of abstracts of the 8th international table tennis federation sports science congress–the 3rd world congress of science and racket sports , 2013, pp. 77–79
2013
Earlier work this paper cites.
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, no. 3, pp. 263–279, 2013
2013
Earlier work this paper cites.
C. Liu, Y. Hayakawa, and A. Nakashima, “Racket control for a table tennis robot to return a ball,” SICE Journal of Control, Measurement, and System Integration , vol. 6, pp. 259–266, 07 2013
2013
Earlier work this paper cites.
Z. Wang, K. Mülling, M. P. Deisenroth, H. B. Amor, D. Vogt, B. Schölkopf, and J. Peters, “Probabilistic movement modeling for intention inference in human–robot interaction,” The International Journal of Robotics Research , vol. 32, no. 7, pp. 841–858, 2013
2013
Earlier work this paper cites.
M. L. Puterman, Markov decision processes: discrete stochastic dynamic programming . John Wiley & Sons, 2014
2014
Earlier work this paper cites.
K. Muelling, A. Boularias, B. Mohler, B. Schölkopf, and J. Peters, “Learning strategies in table tennis using inverse reinforcement learning,” Biol. Cybern. , vol. 108, no. 5, p. 603–619, oct 2014
2014
Earlier work this paper cites.
Y. Huang, B. Schölkopf, and J. Peters, “Learning optimal striking points for a ping-pong playing robot,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2015, pp. 4587–4592
2015
Cited alongside, same era.
A. v. d. Oord, N. Kalchbrenner, O. Vinyals, L. Espeholt, A. Graves, and K. Kavukcuoglu, “Conditional image generation with pixelcnn decoders,” in Proceedings of the 30th International Conference on Neural Information Processing Systems , ser. NIPS’16. Red Hook, NY, USA: Curran Associates Inc., 2016, p. 4797–4805
2016
Cited alongside, same era.
D. Silver, A. Huang, C. Maddison, A. Guez, L. Sifre, G. 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, “Mastering the game of go with deep neural networks and tree search,” Nature , vol. 529, pp. 484–489, 01 2016
2016
Cited alongside, same era.
Y. Jiang, T. Zhang, D. Ho, Y. Bai, C. K. Liu, S. Levine, and J. Tan, “Simgan: Hybrid simulator identification for domain adaptation via adversarial reinforcement learning,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE Press, 2021, p. 2884–2890
2021
Later among the works it cites.
D. Strouse, K. McKee, M. Botvinick, E. Hughes, and R. Everett, “Collaborating with humans without human data,” Advances in Neural Information Processing Systems , vol. 34, pp. 14 502–14 515, 2021
2021
Later among the works it cites.
A. Lupu, B. Cui, H. Hu, and J. Foerster, “Trajectory diversity for zero-shot coordination,” in International conference on machine learning . PMLR, 2021, pp. 7204–7213
2021
Later among the works it cites.
T. Ding, L. Graesser, S. Abeyruwan, D. B. D’Ambrosio, A. Shankar, P. Sermanet, P. R. Sanketi, and C. Lynch, “GoalsEye: Learning High Speed Precision Table Tennis on a Physical Robot,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 10 780–10 787
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. K. Pugh, L. B. Soros, and K. O. Stanley, “Quality diversity: A new frontier for evolutionary computation,” Frontiers in Robotics and AI , vol. 3, p. 40, 2016
2016
Cited alongside, same era.
C. Daniel, G. Neumann, O. Kroemer, and J. Peters, “Hierarchical relative entropy policy search,” Journal of Machine Learning Research , vol. 17, no. 93, pp. 1–50, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2017
Cited alongside, same era.
P. Blank, B. H. Groh, and B. M. Eskofier, “Ball speed and spin estimation in table tennis using a racket-mounted inertial sensor.” in ISWC , S. C. Lee, L. Takayama, K. N. Truong, J. Healey, and T. Ploetz, Eds. ACM, 2017, pp. 2–9
2017
Cited alongside, same era.
Z. Wang, A. Boularias, K. Muelling, B. Schölkopf, and J. Peters, “Anticipatory action selection for human-robot table tennis,” Artif. Intell. , 2017
2017
Cited alongside, same era.
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction , 2nd ed. The MIT Press, 2018
2018
Cited alongside, same era.
T. Osa, J. Pajarinen, G. Neumann, J. A. Bagnell, P. Abbeel, J. Peters et al. , “An algorithmic perspective on imitation learning,” Foundations and Trends® in Robotics , vol. 7, no. 1-2, pp. 1–179, 2018
2018
Cited alongside, same era.
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in Proceedings of the 35th International Conference on Machine Learning . PMLR, 2018, pp. 1861–1870
2018
Cited alongside, same era.
2022
Later among the works it cites.
B. Yang, G. Habibi, P. Lancaster, B. Boots, and J. Smith, “Motivating physical activity via competitive human-robot interaction,” in Conference on Robot Learning . PMLR, 2022, pp. 839–849
2022
Later among the works it cites.
D. Büchler, S. Guist, R. Calandra, V. Berenz, B. Schölkopf, and J. Peters, “Learning to play table tennis from scratch using muscular robots,” IEEE Transactions on Robotics (T-RO) , vol. 38, no. 6, pp. 3850–3860, 2022
2022
Later among the works it cites.
R. Charakorn, P. Manoonpong, and N. Dilokthanakul, “Generating diverse cooperative agents by learning incompatible policies,” in The Eleventh International Conference on Learning Representations , 2022
2022
Later among the works it cites.
B. Cui, A. Lupu, S. Sokota, H. Hu, D. J. Wu, and J. N. Foerster, “Adversarial diversity in hanabi,” in The Eleventh International Conference on Learning Representations , 2022
2022
Later among the works it cites.
C. Yu, J. Gao, W. Liu, B. Xu, H. Tang, J. Yang, Y. Wang, and Y. Wu, “Learning zero-shot cooperation with humans, assuming humans are biased,” in The Eleventh International Conference on Learning Representations , 2022
2022
Later among the works it cites.
B. Tjanaka, M. C. Fontaine, J. Togelius, and S. Nikolaidis, “Approximating gradients for differentiable quality diversity in reinforcement learning,” in Proceedings of the Genetic and Evolutionary Computation Conference , 2022, pp. 1102–1111
2022
Later among the works it cites.
S. Wu, J. Yao, H. Fu, Y. Tian, C. Qian, Y. Yang, Q. Fu, and Y. Wei, “Quality-similar diversity via population based reinforcement learning,” in The Eleventh International Conference on Learning Representations , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Wu, R. Antonova, A. Kan, M. Lepert, A. Zeng, S. Song, J. Bohg, S. Rusinkiewicz, and T. Funkhouser, “Tidybot: personalized robot assistance with large language models,” Auton. Robots , vol. 47, no. 8, p. 1087–1102, nov 2023. [Online]. Available: https://doi.org/10.1007/s10514-023-10139-z
2023
Later among the works it cites.
C. Li, M. Vlastelica, S. Blaes, J. Frey, F. Grimminger, and G. Martius, “Learning agile skills via adversarial imitation of rough partial demonstrations,” in Proceedings of The 6th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, K. Liu, D. Kulic, and J. Ichnowski, Eds., vol. 205. PMLR, 14–18 Dec 2023, pp. 342–352. [Online]. Available: https://proceedings.mlr.press/v205/li23b.html
2023
Later among the works it cites.
S. W. Abeyruwan, L. Graesser, D. B. D’Ambrosio, A. Singh, A. Shankar, A. Bewley, D. Jain, K. M. Choromanski, and P. R. Sanketi, “i-sim2real: Reinforcement learning of robotic policies in tight human-robot interaction loops,” in Conference on Robot Learning . PMLR, 2023, pp. 212–224
2023
Later among the works it cites.
D. B. D’Ambrosio, N. Jaitly, V. Sindhwani, K. Oslund, P. Xu, N. Lazic, A. Shankar, T. Ding, J. Abelian, E. Coumans, G. Kouretas, T. Nguyen, J. Boyd, A. Iscen, R. Mahjourian, V. Vanhoucke, A. Bewley, Y. Kuang, M. Ahn, D. Jain, S. Kataoka, O. E. Cortes, P. Sermanet, C. Lynch, P. R. Sanketi, K. Choromanski, W. Gao, J. Kangaspunta, K. Reymann, G. Vesom, S. Q. Moore, A. Singh, S. W. Abeyruwan, and L. Graesser, “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
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
T. Röfer, T. Laue, A. Hasselbring, J. Lienhoop, Y. Meinken, and P. Reichenberg, “B-human 2022 – more team play with less communication,” in RoboCup 2022: Robot World Cup XXV , A. Eguchi, N. Lau, M. Paetzel-Prüsmann, and T. Wanichanon, Eds. Cham: Springer International Publishing, 2023, pp. 287–299
2023
Later among the works it cites.
Z. Zaidi, D. Martin, N. Belles, V. Zakharov, A. Krishna, K. M. Lee, P. Wagstaff, S. Naik, M. Sklar, S. Choi, Y. Kakehi, R. Patil, D. Mallemadugula, F. Pesce, P. Wilson, W. Hom, M. Diamond, B. Zhao, N. Moorman, R. Paleja, L. Chen, E. Seraj, and M. Gombolay, “Athletic mobile manipulator system for robotic wheelchair tennis,” IEEE Robotics and Automation Letters , vol. 8, no. 4, p. 2245–2252, Apr. 2023. [Online]. Available: http://dx.doi.org/10.1109/LRA.2023.3249401
2023
Later among the works it cites.
S. Abeyruwan, A. Bewley, N. M. Boffi, K. M. Choromanski, D. B. D’Ambrosio, D. Jain, P. R. Sanketi, A. Shankar, V. Sindhwani, S. Singh et al. , “Agile catching with whole-body mpc and blackbox policy learning,” in Learning for Dynamics and Control Conference . PMLR, 2023, pp. 851–863
2023
Later among the works it cites.
E. Kaufmann, L. Bauersfeld, A. Loquercio, M. Mueller, V. Koltun, and D. Scaramuzza, “Champion-level drone racing using deep reinforcement learning,” Nature , vol. 620, pp. 982–987, 08 2023
2023
Later among the works it cites.
N. Sontakke, H. Chae, S. Lee, T. Huang, D. W. Hong, and S. Hal, “Residual physics learning and system identification for sim-to-real transfer of policies on buoyancy assisted legged robots,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2023, pp. 392–399
2023
Later among the works it cites.
B. Sarkar, A. Shih, and D. Sadigh, “Diverse conventions for human-ai collaboration,” in Thirty-seventh Conference on Neural Information Processing Systems , 2023
2023
Later among the works it cites.
R. Zhao, J. Song, Y. Yuan, H. Hu, Y. Gao, Y. Wu, Z. Sun, and W. Yang, “Maximum entropy population-based training for zero-shot human-ai coordination,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, 2023, pp. 6145–6153
2023
Later among the works it cites.
Z. Fu, T. Z. Zhao, and C. Finn, “Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation,” in arXiv , 2024
2024
Closest in time.
“Globe 889 short pips-out table tennis rubber without sponge,” 2024, https://shorturl.at/DQTTV [Last Accessed: (08/01/2024)]
2024
Closest in time.
T. Haarnoja, B. Moran, G. Lever, S. H. Huang, D. Tirumala, J. Humplik, M. Wulfmeier, S. Tunyasuvunakool, N. Y. Siegel, R. Hafner, M. Bloesch, K. Hartikainen, A. Byravan, L. Hasenclever, Y. Tassa, F. Sadeghi, N. Batchelor, F. Casarini, S. Saliceti, C. Game, N. Sreendra, K. Patel, M. Gwira, A. Huber, N. Hurley, F. Nori, R. Hadsell, and N. Heess, “Learning agile soccer skills for a bipedal robot with deep reinforcement learning,” Science Robotics , vol. 9, no. 89, 2024
2024
Closest in time.
“The International Table Tennis Federation Statutes (effective 1st january 2024),” https://documents.ittf.sport/sites/default/files/public/2024-02/2024_ITTF_Statutes_clean_version.pdf , ITTF, Accessed: 08/05/2024
2024
Closest in time.
V. Suriani, E. Musumeci, D. Nardi, and D. D. Bloisi, “Play everywhere: A temporal logic based game environment independent approach for playing soccer with robots,” in RoboCup 2023: Robot World Cup XXVI , C. Buche, A. Rossi, M. Simões, and U. Visser, Eds. Cham: Springer Nature Switzerland, 2024, pp. 3–14
2024
Closest in time.
S. Wang, M. Neau, and C. Buche, “Robonlu: Advancing command understanding with a novel lightweight bert-based approach for service robotics,” in RoboCup 2023: Robot World Cup XXVI , C. Buche, A. Rossi, M. Simões, and U. Visser, Eds. Cham: Springer Nature Switzerland, 2024, pp. 29–41
2024
Closest in time.
2024
Closest in time.