DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning

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2015

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J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz, “Trust Region Policy Optimization,” in Int. Conf. on Machine Learning , 2015, pp. 1889–1897

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D. Belter, P. Łabęcki, and P. Skrzypczyński, “Adaptive motion planning for autonomous rough terrain traversal with a walking robot,” Journal of Field Robotics , pp. 337–370, 2016

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N. Heess, S. Sriram, J. Lemmon, J. Merel, G. Wayne, Y. Tassa, T. Erez, Z. Wang, S. Eslami, M. Riedmiller, and D. Silver, “Emergence of locomotion behaviours in rich environments,” arXiv preprint arXiv:1707.02286 , 2017

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X. B. Peng, G. Berseth, K. Yin, and M. Van De Panne, “DeepLoco: Dynamic Locomotion Skills Using Hierarchical Deep Reinforcement Learning,” ACM Trans. Graph. , pp. 41:1–41:13, 2017

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J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal Policy Optimization Algorithms,” arXiv preprint arXiv:1707.06347 , 2017

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2017

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A. W. Winkler, C. D. Bellicoso, M. Hutter, and J. Buchli, “Gait and trajectory optimization for legged systems through phase-based end-effector parameterization,” IEEE Robotics and Automation Letters , pp. 1560–1567, 2018

2018

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P. Fankhauser, M. Bjelonic, C. D. Bellicoso, T. Miki, and M. Hutter, “Robust rough-terrain locomotion with a quadrupedal robot,” in IEEE Int. Conf. on Robotics and Automation . IEEE, 2018, pp. 1–8

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R. S. Sutton and A. G. Barto, Introduction to Reinforcement Learning , 2nd ed. Cambridge, MA, USA: MIT Press, 2018

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J. Hwangbo, J. Lee, and M. Hutter, “Per-contact iteration method for solving contact dynamics,” IEEE Robotics and Automation Letters , pp. 895–902, 2018

2018

Later among the works it cites.

R. J. Griffin, G. Wiedebach, S. McCrory, S. Bertrand, I. Lee, and J. Pratt, “Footstep planning for autonomous walking over rough terrain,” arXiv preprint arXiv:1907.08673 , 2019

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2019

Closest in time.

O. A. V. Magaña, V. Barasuol, M. Camurri, L. Franceschi, M. Focchi, M. Pontil, D. G. Caldwell, and C. Semini, “Fast and continuous foothold adaptation for dynamic locomotion through CNNs,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 2140–2147, 2019

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Closest in time.

T. Klamt and S. Behnke, “Towards learning abstract representations for locomotion planning in high-dimensional state spaces,” arXiv preprint arXiv:1903.02308 , 2019