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Reinforcement Learning (RL) has seen many recent successes for quadruped robot control.
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J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, p. eaau5872, 2019
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Z. Xie, X. Da, M. van de Panne, B. Babich, and A. Garg, “Dynamics randomization revisited: A case study for quadrupedal locomotion,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 4955–4961
2021
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Z. Li, X. Cheng, X. B. Peng, P. Abbeel, S. Levine, G. Berseth, and K. Sreenath, “Reinforcement learning for robust parameterized locomotion control of bipedal robots,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 2811–2817
2021
Later among the works it cites.
2021
Later among the works it cites.
R. Batke, F. Yu, J. Dao, J. Hurst, R. L. Hatton, A. Fern, and K. Green, “Optimizing bipedal maneuvers of single rigid-body models for reinforcement learning,” in 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) , 2022, pp. 714–721
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2020
Cited alongside, same era.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” 2020. [Online]. Available: https://robotics.sciencemag.org/content/5/47/eabc5986
2020
Cited alongside, same era.
S. Gangapurwala, A. Mitchell, and I. Havoutis, “Guided constrained policy optimization for dynamic quadrupedal robot locomotion,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3642–3649, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
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2020
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F. Grimminger, A. Meduri, M. Khadiv, J. Viereck, M. Wüthrich, M. Naveau, V. Berenz, S. Heim, F. Widmaier, T. Flayols, J. Fiene, A. Badri-Spröwitz, and L. Righetti, “An open torque-controlled modular robot architecture for legged locomotion research,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3650–3657, 2020
2020
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K. Green, Y. Godse, J. Dao, R. L. Hatton, A. Fern, and J. Hurst, “Learning spring mass locomotion: Guiding policies with a reduced-order model,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 3926–3932, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Closest in time.
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 Conference on Robot Learning . PMLR, 2022, pp. 1291–1302
2022
Closest in time.
G. B. Margolis, T. Chen, K. Paigwar, X. Fu, D. Kim, S. bae Kim, and P. Agrawal, “Learning to jump from pixels,” in Conference on Robot Learning . PMLR, 2022, pp. 1025–1034
2022
Closest in time.
M. Bogdanovic, M. Khadiv, and L. Righetti, “Model-free reinforcement learning for robust locomotion using demonstrations from trajectory optimization,” Frontiers in Robotics and AI , vol. 9, 2022. [Online]. Available: https://www.frontiersin.org/articles/10.3389/frobt.2022.854212
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
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 (CoRL) , Dec. 2022
2022
Closest in time.
M. Aractingi, P.-A. Léziart, T. Flayols, J. Perez, T. Silander, and P. Souères, “Controlling the Solo12 Quadruped Robot with Deep Reinforcement Learning,” Aug. 2022, working paper or preprint. [Online]. Available: https://hal.laas.fr/hal-03761331
2022
Closest in time.