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Quadruped robots have shown remarkable mobility on various terrains through reinforcement learning.
2017
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D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell, “Curiosity-driven exploration by self-supervised prediction,” in International conference on machine learning . PMLR, 2017, pp. 2778–2787
2017
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J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2017, pp. 23–30
2017
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2018
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E. Orhan and X. Pitkow, “Skip connections eliminate singularities,” in International Conference on Learning Representations , 2018
2018
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F. Jenelten, J. Hwangbo, F. Tresoldi, C. D. Bellicoso, and M. Hutter, “Dynamic locomotion on slippery ground,” IEEE Robotics and Automation Letters , vol. 4, no. 4, 2019
2019
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Y. Burda, H. Edwards, A. J. Storkey, and O. Klimov, “Exploration by random network distillation,” in 7th International Conference on Learning Representations , 2019
2019
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Y. Burda, H. Edwards, D. Pathak, A. Storkey, T. Darrell, and A. A. Efros, “Large-scale study of curiosity-driven learning,” in International Conference on Learning Representations , 2019
2019
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F. Abdolhosseini, H. Y. Ling, Z. Xie, X. B. Peng, and M. Van de Panne, “On learning symmetric locomotion,” in Proceedings of the 12th ACM SIGGRAPH Conference on Motion, Interaction and Games , 2019, pp. 1–10
2019
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F. Jenelten, T. Miki, A. E. Vijayan, M. Bjelonic, and M. Hutter, “Perceptive locomotion in rough terrain–online foothold optimization,” IEEE Robotics and Automation Letters , vol. 5, no. 4, 2020
2020
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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
2020
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Z. Xie, H. Y. Ling, N. H. Kim, and M. van de Panne, “Allsteps: curriculum-driven learning of stepping stone skills,” in Computer Graphics Forum , vol. 39, no. 8. Wiley Online Library, 2020, pp. 213–224
2020
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V. Tsounis, M. Alge, J. Lee, F. Farshidian, and M. Hutter, “Deepgait: Planning and control of quadrupedal gaits using deep reinforcement learning,” IEEE Robotics and Automation Letters , vol. 5, no. 2, 2020
2020
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C. Yang, K. Yuan, Q. Zhu, W. Yu, and Z. Li, “Multi-expert learning of adaptive legged locomotion,” Science Robotics , vol. 5, no. 49, p. eabb2174, 2020
2020
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M. Laskin, A. Srinivas, and P. Abbeel, “Curl: Contrastive unsupervised representations for reinforcement learning,” in International Conference on Machine Learning . PMLR, 2020, pp. 5639–5650
2020
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O. Melon, R. Orsolino, D. Surovik, M. Geisert, I. Havoutis, and M. Fallon, “Receding-horizon perceptive trajectory optimization for dynamic legged locomotion with learned initialization,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 9805–9811
2021
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A. Agarwal, A. Kumar, J. Malik, and D. Pathak, “Legged locomotion in challenging terrains using egocentric vision,” in 6th Annual Conference on Robot Learning , 2022
2022
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S. Gangapurwala, M. Geisert, R. Orsolino, M. Fallon, and I. Havoutis, “Rloc: Terrain-aware legged locomotion using reinforcement learning and optimal control,” IEEE Transactions on Robotics , 2022
2022
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N. Rudin, D. Hoeller, P. Reist, and M. Hutter, “Learning to walk in minutes using massively parallel deep reinforcement learning,” in Conference on Robot Learning . PMLR, 2022, pp. 91–100
2022
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2022
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V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and G. State, “Isaac gym: High performance GPU based physics simulation for robot learning,” in Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) , 2021
2021
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E. Chane-Sane, C. Schmid, and I. Laptev, “Goal-conditioned reinforcement learning with imagined subgoals,” in International Conference on Machine Learning . PMLR, 2021, pp. 1430–1440
2021
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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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F. Jenelten, R. Grandia, F. Farshidian, and M. Hutter, “Tamols: Terrain-aware motion optimization for legged systems,” IEEE Transactions on Robotics , vol. 38, no. 6, pp. 3395–3413, 2022
2022
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S. Fahmi, V. Barasuol, D. Esteban, O. Villarreal, and C. Semini, “Vital: Vision-based terrain-aware locomotion for legged robots,” IEEE Transactions on Robotics , 2022
2022
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Z. Zhou, B. Wingo, N. Boyd, S. Hutchinson, and Y. Zhao, “Momentum-aware trajectory optimization and control for agile quadrupedal locomotion,” IEEE Robotics and Automation Letters , vol. 7, no. 3, 2022
2022
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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 , vol. 7, no. 62, p. eabk2822, 2022
2022
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M. Tranzatto, T. Miki, M. Dharmadhikari, L. Bernreiter, M. Kulkarni, F. Mascarich, O. Andersson, S. Khattak, M. Hutter, R. Siegwart, et al. , “Cerberus in the darpa subterranean challenge,” Science Robotics , vol. 7, no. 66, p. eabp9742, 2022
2022
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D. Hoeller, N. Rudin, C. Choy, A. Anandkumar, and M. Hutter, “Neural scene representation for locomotion on structured terrain,” IEEE Robotics and Automation Letters , vol. 7, no. 4, 2022
2022
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H. Sun, L. Han, R. Yang, X. Ma, J. Guo, and B. Zhou, “Optimistic curiosity exploration and conservative exploitation with linear reward shaping,” in Advances in Neural Information Processing Systems , 2022
2022
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C. Zhang, W. Yu, and Z. Li, “Accessibility-based clustering for efficient learning of locomotion skills,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 1600–1606
2022
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R. Grandia, F. Jenelten, S. Yang, F. Farshidian, and M. Hutter, “Perceptive locomotion through nonlinear model-predictive control,” IEEE Transactions on Robotics , 2023
2023
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2023
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Z. Xie, X. Da, B. Babich, A. Garg, and M. v. de Panne, “Glide: Generalizable quadrupedal locomotion in diverse environments with a centroidal model,” in International Workshop on the Algorithmic Foundations of Robotics . Springer, 2023, pp. 523–539
2023
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2024
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