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Reinforcement learning combined with sim-to-real transfer offers a general framework for developing locomotion controllers for legged robots.
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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
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X. B. Peng, Z. Ma, P. Abbeel, S. Levine, and A. Kanazawa, “Amp: adversarial motion priors for stylized physics-based character control,” ACM Transactions on Graphics , vol. 40, no. 4, p. 1–20, July 2021. [Online]. Available: http://dx.doi.org/10.1145/3450626.3459670
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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
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Y. Ji, Z. Li, Y. Sun, X. B. Peng, S. Levine, G. Berseth, and K. Sreenath, “Hierarchical reinforcement learning for precise soccer shooting skills using a quadrupedal robot,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 1479–1486
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A. Kumar, Z. Li, J. Zeng, D. Pathak, K. Sreenath, and J. Malik, “Adapting rapid motor adaptation for bipedal robots,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 1161–1168
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Z. Fu, X. Cheng, and D. Pathak, “Deep whole-body control: learning a unified policy for manipulation and locomotion,” in Conference on Robot Learning . PMLR, 2023, pp. 138–149
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H. Lai, W. Zhang, X. He, C. Yu, Z. Tian, Y. Yu, and J. Wang, “Sim-to-real transfer for quadrupedal locomotion via terrain transformer,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5141–5147
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I. Radosavovic, T. Xiao, B. Zhang, T. Darrell, J. Malik, and K. Sreenath, “Real-world humanoid locomotion with reinforcement learning,” Science Robotics , vol. 9, no. 89, p. eadi9579, 2024
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