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Transferring human motion skills to humanoid robots remains a significant challenge.
2009
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2011
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2012
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P. M. Wensing and D. E. Orin, “High-speed humanoid running through control with a 3d-slip model,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2013, pp. 5134–5140
2013
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2015
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2015
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J. Ho and S. Ermon, “Generative adversarial imitation learning,” Advances in neural information processing systems , vol. 29, 2016
2016
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T. Kamioka, H. Kaneko, M. Kuroda, C. Tanaka, S. Shirokura, M. Takeda, and T. Yoshiike, “Dynamic gait transition between walking, running and hopping for push recovery,” in 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) . IEEE, 2017, pp. 1–8
2017
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2017
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Y. Mroueh and T. Sercu, “Fisher gan,” Advances in neural information processing systems , vol. 30, 2017
2017
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I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” Advances in neural information processing systems , vol. 30, 2017
2017
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K. Ayusawa and E. Yoshida, “Motion retargeting for humanoid robots based on simultaneous morphing parameter identification and motion optimization,” IEEE Transactions on Robotics , vol. 33, no. 6, pp. 1343–1357, 2017
2017
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2017
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W. Yu, G. Turk, and C. K. Liu, “Learning symmetric and low-energy locomotion,” ACM Transactions on Graphics (TOG) , vol. 37, no. 4, pp. 1–12, 2018
2018
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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
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S. Starke, N. Hendrich, and J. Zhang, “Memetic evolution for generic full-body inverse kinematics in robotics and animation,” IEEE Transactions on Evolutionary Computation , vol. 23, no. 3, pp. 406–420, 2018
2018
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2018
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H. Petzka, A. Fischer, and D. Lukovnikov, “On the regularization of wasserstein gans,” in International Conference on Learning Representations , 2018
2018
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K. Ishihara, T. D. Itoh, and J. Morimoto, “Full-body optimal control toward versatile and agile behaviors in a humanoid robot,” IEEE Robotics and Automation Letters , vol. 5, no. 1, pp. 119–126, 2019
2019
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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, p. eaau5872, 2019
2019
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Y. Kojio, Y. Ishiguro, F. Sugai, Y. Kakiuchi, K. Okada, M. Inaba et al. , “Unified balance control for biped robots including modification of footsteps with angular momentum and falling detection based on capturability,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 497–504
2019
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Z. Xie, P. Clary, J. Dao, P. Morais, J. Hurst, and M. Panne, “Learning locomotion skills for cassie: Iterative design and sim-to-real,” in Conference on Robot Learning . PMLR, 2020, pp. 317–329
2020
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2020
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K. Aberman, P. Li, D. Lischinski, O. Sorkine-Hornung, D. Cohen-Or, and B. Chen, “Skeleton-aware networks for deep motion retargeting,” ACM Transactions on Graphics (TOG) , vol. 39, no. 4, pp. 62–1, 2020
2022
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D. Kang, F. De Vincenti, N. C. Adami, and S. Coros, “Animal motions on legged robots using nonlinear model predictive control,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 11 955–11 962
2022
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X. B. Peng, Y. Guo, L. Halper, S. Levine, and S. Fidler, “Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters,” ACM Transactions On Graphics (TOG) , vol. 41, no. 4, pp. 1–17, 2022
2022
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A. Escontrela, X. B. Peng, W. Yu, T. Zhang, A. Iscen, K. Goldberg, and P. Abbeel, “Adversarial motion priors make good substitutes for complex reward functions,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 25–32
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2020
Cited alongside, same era.
M. Zhang, Y. Wang, X. Ma, L. Xia, J. Yang, Z. Li, and X. Li, “Wasserstein distance guided adversarial imitation learning with reward shape exploration,” in 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) . IEEE, 2020, pp. 1165–1170
2020
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Y. Kojio, Y. Omori, K. Kojima, F. Sugai, Y. Kakiuchi, K. Okada, and M. Inaba, “Footstep modification including step time and angular momentum under disturbances on sparse footholds,” IEEE Robotics and Automation Letters , vol. 5, no. 3, pp. 4907–4914, 2020
2020
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T. Sugihara, K. Imanishi, T. Yamamoto, and S. Caron, “3d biped locomotion control including seamless transition between walking and running via 3d zmp manipulation,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 6258–6263
2021
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M. Chignoli, D. Kim, E. Stanger-Jones, and S. Kim, “The mit humanoid robot: Design, motion planning, and control for acrobatic behaviors,” in 2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids) . IEEE, 2021, pp. 1–8
2021
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J. Siekmann, Y. Godse, A. Fern, and J. Hurst, “Sim-to-real learning of all common bipedal gaits via periodic reward composition,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 7309–7315
2021
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R. Dadashi, L. Hussenot, M. Geist, and O. Pietquin, “Primal wasserstein imitation learning,” in ICLR 2021-Ninth International Conference on Learning Representations , 2021
2021
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I. Durugkar, M. Tec, S. Niekum, and P. Stone, “Adversarial intrinsic motivation for reinforcement learning,” Advances in Neural Information Processing Systems , vol. 34, pp. 8622–8636, 2021
2021
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2021
Cited alongside, same era.
2022
Later among the works it cites.
S. Sato, Y. Kojio, Y. Kakiuchi, K. Kojima, K. Okada, and M. Inaba, “Robust humanoid walking system considering recognized terrain and robots’ balance,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 8298–8305
2022
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2023
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2023
Closest in time.
D. Kim, G. Berseth, M. Schwartz, and J. Park, “Torque-based deep reinforcement learning for task-and-robot agnostic learning on bipedal robots using sim-to-real transfer,” IEEE Robotics and Automation Letters , vol. 8, no. 10, pp. 6251–6258, 2023
2023
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D. Crowley, J. Dao, H. Duan, K. Green, J. Hurst, and A. Fern, “Optimizing bipedal locomotion for the 100m dash with comparison to human running,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 12 205–12 211
2023
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C. Li, M. Vlastelica, S. Blaes, J. Frey, F. Grimminger, and G. Martius, “Learning agile skills via adversarial imitation of rough partial demonstrations,” in Conference on Robot Learning . PMLR, 2023, pp. 342–352
2023
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2023
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S. H. Jeon, S. Heim, C. Khazoom, and S. Kim, “Benchmarking potential based rewards for learning humanoid locomotion,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 9204–9210
2023
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R. P. Singh, Z. Xie, P. Gergondet, and F. Kanehiro, “Learning bipedal walking for humanoids with current feedback,” IEEE Access , 2023
2023
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2023
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2023
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E. Vollenweider, M. Bjelonic, V. Klemm, N. Rudin, J. Lee, and M. Hutter, “Advanced skills through multiple adversarial motion priors in reinforcement learning,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5120–5126
2023
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J. Wu, G. Xin, C. Qi, and Y. Xue, “Learning robust and agile legged locomotion using adversarial motion priors,” IEEE Robotics and Automation Letters , 2023
2023
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2023
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R. Grandia, F. Farshidian, E. Knoop, C. Schumacher, M. Hutter, and M. Bächer, “Doc: Differentiable optimal control for retargeting motions onto legged robots,” ACM Transactions on Graphics (TOG) , vol. 42, no. 4, pp. 1–14, 2023
2023
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