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Legged robots are becoming increasingly agile in exhibiting dynamic behaviors such as running and jumping.
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M. Ajallooeian, S. Pouya, A. Sproewitz, and A. J. Ijspeert, “Central pattern generators augmented with virtual model control for quadruped rough terrain locomotion,” in 2013 IEEE International Conference on Robotics and Automation , 2013, pp. 3321–3328
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N. Rudin, D. Hoeller, M. Bjelonic, and M. Hutter, “Advanced skills by learning locomotion and local navigation end-to-end,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 2497–2503
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T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,” Science Robotics , 2022
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2022
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T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, “Optuna: A next-generation hyperparameter optimization framework,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2019
2019
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G. Bellegarda and K. Byl, “An online training method for augmenting mpc with deep reinforcement learning,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 5453–5459
2020
Cited alongside, same era.
2020
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M. Chignoli and S. Kim, “Online trajectory optimization for dynamic aerial motions of a quadruped robot,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 7693–7699
2021
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M. Sombolestan, Y. Chen, and Q. Nguyen, “Adaptive force-based control for legged robots,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021, pp. 7440–7447
2021
Cited alongside, same era.
G. B. Margolis, T. Chen, K. Paigwar, X. Fu, D. Kim, S. bae Kim, and P. Agrawal, “Learning to jump from pixels,” in 5th Annual Conference on Robot Learning , 2021
2021
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W. Yu, D. Jain, A. Escontrela, A. Iscen, P. Xu, E. Coumans, S. Ha, J. Tan, and T. Zhang, “Visual-locomotion: Learning to walk on complex terrains with vision,” in 5th Annual Conference on Robot Learning , 2021
2021
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M. S. Ashtiani, A. Aghamaleki Sarvestani, and A. Badri-Spröwitz, “Hybrid parallel compliance allows robots to operate with sensorimotor delays and low control frequencies,” Frontiers in Robotics and AI , vol. 8, p. 645748, 2021
2021
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2022
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L. Smith, J. C. Kew, X. B. Peng, S. Ha, J. Tan, and S. Levine, “Legged robots that keep on learning: Fine-tuning locomotion policies in the real world,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 1593–1599
2022
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F. Ruppert and A. Badri-Spröwitz, “Learning plastic matching of robot dynamics in closed-loop central pattern generators,” Nature Machine Intelligence , vol. 4, no. 7, pp. 652–660, 2022
2022
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J. Fan, Q. Du, Z. Dong, J. Zhao, and T. Xu, “Design of the jump mechanism for a biomimetic robotic frog,” Biomimetics , vol. 7, no. 4, p. 142, 2022
2022
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S. H. Jeon, S. Kim, and D. Kim, “Online optimal landing control of the mit mini cheetah,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 178–184
2022
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Z. Li, X. B. Peng, P. Abbeel, S. Levine, G. Berseth, and K. Sreenath, “Robust and Versatile Bipedal Jumping Control through Reinforcement Learning,” in Proceedings of Robotics: Science and Systems , Daegu, Republic of Korea, July 2023
2023
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2023
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2023
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Y. Yang, X. Meng, W. Yu, T. Zhang, J. Tan, and B. Boots, “Continuous versatile jumping using learned action residuals,” in Proceedings of The 5th Annual Learning for Dynamics and Control Conference , ser. Proceedings of Machine Learning Research, N. Matni, M. Morari, and G. J. Pappas, Eds., vol. 211. PMLR, 15–16 Jun 2023, pp. 770–782
2023
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2023
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M. Shafiee, G. Bellegarda, and A. Ijspeert, “Puppeteer and marionette: Learning anticipatory quadrupedal locomotion based on interactions of a central pattern generator and supraspinal drive,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 1112–1119
2023
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2023
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2023
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