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Humanoid robots are designed to navigate environments accessible to humans using their legs.
Real-time humanoid motion generation through zmp manipulation based on inverted pendulum control
Tomomichi Sugihara, Yoshihiko Nakamura, and Hirochika Inoue · 2002
Earlier work this paper cites.
Zero-moment point—thirty five years of its life
Miomir Vukobratović and Branislav Borovac · 2004
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Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot
Scott Kuindersma, Robin Deits, Maurice Fallon, Andrés Valenzuela, Hongkai Dai, Frank Permenter, Twan Koolen, Pat Marion, and Russ Tedrake · 2016
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Amass: Archive of motion capture as surface shapes
Naureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, and Michael J. Black · 2019
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Fast transformer decoding: One write-head is all you need
Noam Shazeer · 2019
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Learning quadrupedal locomotion over challenging terrain
Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter · 2020
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Mpc for humanoid gait generation: Stability and feasibility
Nicola Scianca, Daniele De Simone, Leonardo Lanari, and Giuseppe Oriolo · 2020
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Rma: Rapid motor adaptation for legged robots
Ashish Kumar, Zipeng Fu, Deepak Pathak, and Jitendra Malik · 2021
Earlier work this paper cites.
Amp: Adversarial motion priors for stylized physics-based character control
Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, and Angjoo Kanazawa · 2021
Earlier work this paper cites.
Blind bipedal stair traversal via sim-to-real reinforcement learning
Jonah Siekmann, Kevin Green, John Warila, Alan Fern, and Jonathan Hurst · 2021
Cited alongside, same era.
Sparsity winning twice: Better robust generalization from more efficient training
Tianlong Chen, Zhenyu Zhang, Pengjun Wang, Santosh Balachandra, Haoyu Ma, Zehao Wang, and Zhangyang Wang · 2022
Cited alongside, same era.
Concurrent training of a control policy and a state estimator for dynamic and robust legged locomotion
Gwanghyeon Ji, Juhyeok Mun, Hyeongjun Kim, and Jemin Hwangbo · 2022
Cited alongside, same era.
Linear policies are sufficient to realize robust bipedal walking on challenging terrains
Lokesh Krishna, Guillermo A Castillo, Utkarsh A Mishra, Ayonga Hereid, and Shishir Kolathaya · 2022
Cited alongside, same era.
Adapting rapid motor adaptation for bipedal robots
Ashish Kumar, Zhongyu Li, Jun Zeng, Deepak Pathak, Koushil Sreenath, and Jitendra Malik · 2022
Identifying terrain physical parameters from vision-towards physical-parameter-aware locomotion and navigation
Jiaqi Chen, Jonas Frey, Ruyi Zhou, Takahiro Miki, Georg Martius, and Marco Hutter · 2024
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Expressive whole-body control for humanoid robots
Xuxin Cheng, Yandong Ji, Junming Chen, Ruihan Yang, Ge Yang, and Xiaolong Wang · 2024
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Anymal parkour: Learning agile navigation for quadrupedal robots
David Hoeller, Nikita Rudin, Dhionis Sako, and Marco Hutter · 2024
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Dtc: Deep tracking control
Fabian Jenelten, Junzhe He, Farbod Farshidian, and Marco Hutter · 2024
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Reinforcement learning for versatile, dynamic, and robust bipedal locomotion control
Zhongyu Li, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, and Koushil Sreenath · 2024
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Cited alongside, same era.
Learning robust perceptive locomotion for quadrupedal robots in the wild
Takahiro Miki, Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter · 2022
Cited alongside, same era.
Orbit: A unified simulation framework for interactive robot learning environments
Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu, Nikita Rudin, David Hoeller, Jia Lin Yuan, Ritvik Singh, Yunrong Guo, Hammad Mazhar, Ajay Mandlekar, Buck Babich, Gavriel State, Marco Hutter, and Animesh Garg · 2023
Cited alongside, same era.
Dreamwaq: Learning robust quadrupedal locomotion with implicit terrain imagination via deep reinforcement learning
I Made Aswin Nahrendra, Byeongho Yu, and Hyun Myung · 2023
Cited alongside, same era.
Real-world humanoid locomotion with reinforcement learning
Ilija Radosavovic, Tete Xiao, Bike Zhang, Trevor Darrell, Jitendra Malik, and Koushil Sreenath · 2023
Cited alongside, same era.
Ziwen Zhuang, Zipeng Fu, Jianren Wang, Christopher Atkeson, Soeren Schwertfeger, Chelsea Finn, and Hang Zhao · 2023
Cited alongside, same era.
Humanoid-gym: Reinforcement learning for humanoid robot with zero-shot sim2real transfer
Xinyang Gu, Yen-Jen Wang, and Jianyu Chen
Cited in the paper.
Advancing humanoid locomotion: Mastering challenging terrains with denoising world model learning
Xinyang Gu, Yen-Jen Wang, Xiang Zhu, Chengming Shi, Yanjiang Guo, Yichen Liu, and Jianyu Chen
Cited in the paper.
Junfeng Long, Junli Ren, Moji Shi, Zirui Wang, Tao Huang, Ping Luo, and Jiangmiao Pang · 2024
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Humanmimic: Learning natural locomotion and transitions for humanoid robot via wasserstein adversarial imitation
Annan Tang, Takuma Hiraoka, Naoki Hiraoka, Fan Shi, Kento Kawaharazuka, Kunio Kojima, Kei Okada, and Masayuki Inaba · 2024
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Revisiting reward design and evaluation for robust humanoid standing and walking
Bart van Marum, Aayam Shrestha, Helei Duan, Pranay Dugar, Jeremy Dao, and Alan Fern · 2024
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Ziwen Zhuang, Shenzhe Yao, and Hang Zhao · 2024
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