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Previous humanoid robot research works treat the robot as a bipedal mobile manipulation platform, where only the feet and hands contact the environment.
The 3d linear inverted pendulum mode: a simple modeling for a biped walking pattern generation
S. Kajita, F. Kanehiro, K. Kaneko, K. Yokoi, and H. Hirukawa · 2001
Earlier work this paper cites.
Whole body humanoid control from human motion descriptors
Behzad Dariush, Michael Gienger, Bing Jian, Christian Goerick, and Kikuo Fujimura · 2008
Earlier work this paper cites.
A unified framework for whole-body humanoid robot control with multiple constraints and contacts
Oussama Khatib, Luis Sentis, and Jaeheung Park · 2008
Earlier work this paper cites.
Mabel, a new robotic bipedal walker and runner
J.W. Grizzle, Jonathan Hurst, Benjamin Morris, Hae-Won Park, and Koushil Sreenath · 2009
Earlier work this paper cites.
High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, and P. Abbeel · 2015
Earlier work this paper cites.
Unifying representations and large-scale whole-body motion databases for studying human motion
Christian Mandery, Ömer Terlemez, Martin Do, Nikolaus Vahrenkamp, and Tamim Asfour · 2016
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Mit cheetah 3: Design and control of a robust, dynamic quadruped robot
Gerardo Bledt, Matthew J. Powell, Benjamin Katz, Jared Di Carlo, Patrick M. Wensing, and Sangbae Kim · 2018
Earlier work this paper cites.
Deepmimic: Example-guided deep reinforcement learning of physics-based character skills
Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne · 2018
Earlier work this paper cites.
AMASS: Archive of motion capture as surface shapes
Naureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, and Michael J. Black · 2019
Earlier work this paper cites.
Whole-Body Control of Humanoid Robots , pages 1161–1183
Federico L. Moro and Luis Sentis · 2019
Earlier work this paper cites.
The PETMAN and Atlas Robots at Boston Dynamics , pages 169–186
Gabe Nelson, Aaron Saunders, and Robert Playter · 2019
Earlier work this paper cites.
Learning quadrupedal locomotion over challenging terrain
Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter · 2020
Earlier work this paper cites.
A multi-critic reinforcement learning method: An application to multi-tank water systems
Juan Martinez-Piazuelo, Daniel E. Ochoa, Nicanor Quijano, and Luis Felipe Giraldo · 2020
Earlier work this paper cites.
Robust feedback motion policy design using reinforcement learning on a 3d digit bipedal robot
Guillermo A. Castillo, Bowen Weng, Wei Zhang, and Ayonga Hereid · 2021
Earlier work this paper cites.
The mit humanoid robot: Design, motion planning, and control for acrobatic behaviors
Matthew Chignoli, Donghyun Kim, Elijah Stanger-Jones, and Sangbae Kim · 2021
Earlier work this paper cites.
Onnx runtime
ONNX Runtime developers · 2021
Earlier work this paper cites.
Rma: Rapid motor adaptation for legged robots
Ashish Kumar, Zipeng Fu, Deepak Pathak, and Jitendra Malik · 2021
Cited alongside, same era.
Learning to walk in minutes using massively parallel deep reinforcement learning
Nikita Rudin, David Hoeller, Philipp Reist, and Marco Hutter · 2021
Cited alongside, same era.
Real-time optimal navigation planning using learned motion costs
Bowen Yang, Lorenz Wellhausen, Takahiro Miki, Ming Liu, and Marco Hutter · 2021
Cited alongside, same era.
Legged locomotion in challenging terrains using egocentric vision
Ananye Agarwal, Ashish Kumar, Jitendra Malik, and Deepak Pathak · 2022
Cited alongside, same era.
Adversarial motion priors make good substitutes for complex reward functions
Alejandro Escontrela, Xue Bin Peng, Wenhao Yu, Tingnan Zhang, Atil Iscen, Ken Goldberg, and Pieter Abbeel · 2022
Cited alongside, same era.
Combining learning-based locomotion policy with model-based manipulation for legged mobile manipulators
Humanoid-gym: Reinforcement learning for humanoid robot with zero-shot sim2real transfer
Xinyang Gu, Yen-Jen Wang, and Jianyu Chen · 2024
Later among the works it cites.
Anymal parkour: Learning agile navigation for quadrupedal robots
David Hoeller, Nikita Rudin, Dhionis Sako, and Marco Hutter · 2024
Later among the works it cites.
Learning agile bipedal motions on a quadrupedal robot
Yunfei Li, Jinhan Li, Wei Fu, and Yi Wu · 2024
Later among the works it cites.
Berkeley humanoid: A research platform for learning-based control, 2024
Qiayuan Liao, Bike Zhang, Xuanyu Huang, Xiaoyu Huang, Zhongyu Li, and Koushil Sreenath · 2024
Later among the works it cites.
Rdt-1b: a diffusion foundation model for bimanual manipulation
Songming Liu, Lingxuan Wu, Bangguo Li, Hengkai Tan, Huayu Chen, Zhengyi Wang, Ke Xu, Hang Su, and Jun Zhu · 2024
Later among the works it cites.
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Yuntao Ma, Farbod Farshidian, Takahiro Miki, Joonho Lee, and Marco Hutter · 2022
Cited alongside, same era.
Walk these ways: Tuning robot control for generalization with multiplicity of behavior
Gabriel B Margolis and Pulkit Agrawal · 2022
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.
Extreme parkour with legged robots
Xuxin Cheng, Kexin Shi, Ananye Agarwal, and Deepak Pathak · 2023
Cited alongside, same era.
Synchronized human-humanoid motion imitation
Antonin Dallard, Mehdi Benallegue, Fumio Kanehiro, and Abderrahmane Kheddar · 2023
Cited alongside, same era.
Humans in 4D: Reconstructing and tracking humans with transformers
Shubham Goel, Georgios Pavlakos, Jathushan Rajasegaran, Angjoo Kanazawa, and Jitendra Malik · 2023
Cited alongside, same era.
Perpetual humanoid control for real-time simulated avatars
Zhengyi Luo, Jinkun Cao, Alexander W. Winkler, Kris Kitani, and Weipeng Xu · 2023
Cited alongside, same era.
Learning humanoid locomotion with perceptive internal model, 2024
Junfeng Long, Junli Ren, Moji Shi, Zirui Wang, Tao Huang, Ping Luo, and Jiangmiao Pang · 2024
Later among the works it cites.
Mobile-television: Predictive motion priors for humanoid whole-body control
Chenhao Lu, Xuxin Cheng, Jialong Li, Shiqi Yang, Mazeyu Ji, Chengjing Yuan, Ge Yang, Sha Yi, and Xiaolong Wang · 2024
Later among the works it cites.
Learning to walk in confined spaces using 3d representation
Takahiro Miki, Joonho Lee, Lorenz Wellhausen, and Marco Hutter · 2024
Later among the works it cites.
Humanoid locomotion as next token prediction
Ilija Radosavovic, Bike Zhang, Baifeng Shi, Jathushan Rajasegaran, Sarthak Kamat, Trevor Darrell, Koushil Sreenath, and Jitendra Malik · 2024
Later among the works it cites.
Vmp: Versatile motion priors for robustly tracking motion on physical characters
Agon Serifi, Ruben Grandia, Espen Knoop, Markus Gross, and Moritz Bächer · 2024
Later among the works it cites.
Maskedmimic: Unified physics-based character control through masked motion inpainting
Chen Tessler, Yunrong Guo, Ofir Nabati, Gal Chechik, and Xue Bin Peng · 2024
Later among the works it cites.
Revisiting reward design and evaluation for robust human standing and walking
Bart van Marum, Aayam Shrestha, Helei Duan, Pranay Dugar, Jeremy Dao, and Alan Fern · 2024
Later among the works it cites.
Boxi Xia, Bokuan Li, Jacob Lee, Michael Scutari, and Boyuan Chen · 2024
Later among the works it cites.
Robotkeyframing: Learning locomotion with high-level objectives via mixture of dense and sparse rewards
Fatemeh Zargarbashi, Jin Cheng, Dongho Kang, Robert Sumner, and Stelian Coros · 2024
Later among the works it cites.
PyTorch Kinematics, February 2024
Sheng Zhong, Thomas Power, Ashwin Gupta, and Peter Mitrano · 2024
Later among the works it cites.
Humanoid parkour learning
Ziwen Zhuang, Shenzhe Yao, and Hang Zhao · 2024
Later among the works it cites.