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In recent years, reinforcement learning (RL) has shown outstanding performance for locomotion control of highly articulated robotic systems.
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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,” 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
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. [Online]. Available: https://www.science.org/doi/abs/10.1126/scirobotics.aau5872
2019
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, p. eabc5986, 2020. [Online]. Available: https://www.science.org/doi/abs/10.1126/scirobotics.abc5986
2020
Cited alongside, same era.
2021
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,” 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
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 , vol. 7, no. 62, 2022
2022
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M. Bjelonic, R. Grandia, M. Geilinger, O. Harley, V. S. Medeiros, V. Pajovic, S. Edo, Jelavic Coros, and M. Hutter, “Complex motion decomposition: combining offline motion libraries with online MPC,” under review for The International Journal of Robotics Research , 2022
2022
Closest in time.