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Learning a locomotion policy for quadruped robots has traditionally been constrained to a specific robot morphology, mass, and size.
G. Feng, H. Zhang, Z. Li, X. B. Peng, B. Basireddy, L. Yue, Z. Song, L. Yang, Y. Liu, K. Sreenath, et al. , “Genloco: Generalized locomotion controllers for quadrupedal robots,” in Conference on Robot Learning . PMLR, 2023, pp. 1893–1903
1903
Earlier work this paper cites.
I. A. Rybak, N. A. Shevtsova, M. Lafreniere-Roula, and D. A. McCrea, “Modelling spinal circuitry involved in locomotor pattern generation: insights from deletions during fictive locomotion,” The Journal of physiology , vol. 577, no. 2, pp. 617–639, 2006
2006
Earlier work this paper cites.
H. Kimura, Y. Fukuoka, and A. H. Cohen, “Adaptive dynamic walking of a quadruped robot on natural ground based on biological concepts,” The International Journal of Robotics Research , vol. 26, no. 5, pp. 475–490, 2007
2007
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A. J. Ijspeert, A. Crespi, D. Ryczko, and J.-M. Cabelguen, “From swimming to walking with a salamander robot driven by a spinal cord model,” Science , vol. 315, no. 5817, pp. 1416–1420, 2007
2007
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A. J. Ijspeert, “Central pattern generators for locomotion control in animals and robots: A review,” Neural Networks , vol. 21, no. 4, pp. 642–653, 2008, robotics and Neuroscience
2008
Earlier work this paper cites.
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
2013
Earlier work this paper cites.
A. Spröwitz, A. Tuleu, M. Vespignani, M. Ajallooeian, E. Badri, and A. J. Ijspeert, “Towards dynamic trot gait locomotion: Design, control, and experiments with cheetah-cub, a compliant quadruped robot,” The International Journal of Robotics Research , vol. 32, no. 8, pp. 932–950, 2013
2013
Earlier work this paper cites.
S. Aoi, P. Manoonpong, Y. Ambe, F. Matsuno, and F. Wörgötter, “Adaptive control strategies for interlimb coordination in legged robots: a review,” Frontiers in neurorobotics , vol. 11, p. 39, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
S. Grillner, “Evolution: vertebrate limb control over 420 million years,” Current Biology , vol. 28, no. 4, pp. R162–R164, 2018
2018
Earlier work this paper cites.
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
2018
Earlier work this paper cites.
A. Iscen, K. Caluwaerts, J. Tan, T. Zhang, E. Coumans, V. Sindhwani, and V. Vanhoucke, “Policies modulating trajectory generators,” in Conference on Robot Learning . PMLR, 2018, pp. 916–926
2018
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A. Fukuhara, D. Owaki, T. Kano, R. Kobayashi, and A. Ishiguro, “Spontaneous gait transition to high-speed galloping by reconciliation between body support and propulsion,” Advanced robotics , vol. 32, no. 15, pp. 794–808, 2018
2018
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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, 2019
2019
Earlier work this paper cites.
S. Grillner and A. El Manira, “Current principles of motor control, with special reference to vertebrate locomotion,” Physiological reviews , vol. 100, no. 1, pp. 271–320, 2020
2020
Cited alongside, same era.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science Robotics , vol. 5, no. 47, 2020
2020
Cited alongside, same era.
X. B. Peng, E. Coumans, T. Zhang, T.-W. Lee, J. Tan, and S. Levine, “Learning Agile Robotic Locomotion Skills by Imitating Animals,” in Proceedings of Robotics: Science and Systems , Corvalis, Oregon, USA, July 2020
2020
Cited alongside, same era.
W. Huang, I. Mordatch, and D. Pathak, “One policy to control them all: Shared modular policies for agent-agnostic control,” in International Conference on Machine Learning . PMLR, 2020, pp. 4455–4464
2020
Cited alongside, same era.
G. Bellegarda, Y. Chen, Z. Liu, and Q. Nguyen, “Robust high-speed running for quadruped robots via deep reinforcement learning,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, pp. 10 364–10 370
2022
Later among the works it cites.
Y. Yang, T. Zhang, E. Coumans, J. Tan, and B. Boots, “Fast and efficient locomotion via learned gait transitions,” in Conference on Robot Learning . PMLR, 2022, pp. 773–783
2022
Later among the works it cites.
Y. Shao, Y. Jin, X. Liu, W. He, H. Wang, and W. Yang, “Learning free gait transition for quadruped robots via phase-guided controller,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 1230–1237, 2022
2022
Later among the works it cites.
A. S. Chiappa, A. Marin Vargas, and A. Mathis, “Dmap: a distributed morphological attention policy for learning to locomote with a changing body,” Advances in Neural Information Processing Systems , vol. 35, pp. 37 214–37 227, 2022
2022
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2020
Cited alongside, same era.
R. Thandiackal, K. Melo, L. Paez, J. Herault, T. Kano, K. Akiyama, F. Boyer, D. Ryczko, A. Ishiguro, and A. J. Ijspeert, “Emergence of robust self-organized undulatory swimming based on local hydrodynamic force sensing,” Science Robotics , vol. 6, no. 57, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
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) . IEEE, 2021, pp. 7440–7447
2021
Cited alongside, same era.
T. Miki, J. Lee, J. Hwanbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,” Science Robotics , 2022
2022
Cited alongside, same era.
G. Ji, J. Mun, H. Kim, and J. Hwangbo, “Concurrent training of a control policy and a state estimator for dynamic and robust legged locomotion,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4630–4637, 2022
2022
Cited alongside, same era.
B. Trabucco, M. Phielipp, and G. Berseth, “Anymorph: Learning transferable polices by inferring agent morphology,” in International Conference on Machine Learning . PMLR, 2022, pp. 21 677–21 691
2022
Later among the works it cites.
G. Bellegarda and A. Ijspeert, “CPG-RL: Learning central pattern generators for quadruped locomotion,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 12 547–12 554, 2022
2022
Later among the works it cites.
N. Rudin, D. Hoeller, P. Reist, and M. Hutter, “Learning to walk in minutes using massively parallel deep reinforcement learning,” in Proceedings of the 5th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, A. Faust, D. Hsu, and G. Neumann, Eds., vol. 164. PMLR, 08–11 Nov 2022, pp. 91–100. [Online]. Available: https://proceedings.mlr.press/v164/rudin22a.html
2022
Later among the works it cites.
W. Yu, C. Yang, C. McGreavy, E. Triantafyllidis, G. Bellegarda, M. Shafiee, A. J. Ijspeert, and Z. Li, “Identifying important sensory feedback for learning locomotion skills,” Nature Machine Intelligence , vol. 5, no. 8, pp. 919–932, 2023
2023
Closest in time.
J. Whitman, M. Travers, and H. Choset, “Learning modular robot control policies,” IEEE Transactions on Robotics , 2023
2023
Closest in time.
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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G. Bellegarda, M. Shafiee, M. E. Özberk, and A. Ijspeert, “Quadruped-Frog: Rapid online optimization of continuous quadruped jumping,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024
2024
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G. Bellegarda, M. Shafiee, and A. Ijspeert, “Visual CPG-RL: Learning central pattern generators for visually-guided quadruped locomotion,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024
2024
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