OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning
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Cited by 40 in Deep reinforcement learning methods, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We present OmniH2O (Omni Human-to-Humanoid), a learning-based system for whole-body humanoid teleoperation and autonomy. Using kinematic pose as a universal control interface, OmniH2O enables various ways for a human to control a full-sized humanoid with dexterous hands, including using real-time teleoperation through VR headset, verbal instruction, and RGB camera. OmniH2O also enables full autonomy by learning from teleoperated demonstrations or integrating with frontier models such as GPT-4. OmniH2O demonstrates versatility and dexterity in various real-world whole-body tasks through teleoperation or autonomy, such as playing multiple sports, moving and manipulating objects, and interacting with humans. We develop an RL-based sim-to-real pipeline, which involves large-scale retargeting and augmentation of human motion datasets, learning a real-world deployable policy with sparse sensor input by imitating a privileged teacher policy, and reward designs to enhance robustness and stability. We release the first humanoid whole-body control dataset, OmniH2O-6, containing six everyday tasks, and demonstrate humanoid whole-body skill learning from teleoperated datasets.
Key facts. Cited by 189 papers in the map (data through 14 Oct 2026). Builds on 40 of its 63 references that are in the map. First posted on arXiv 13 Jun 2024.
Builds on
40 of 63 references.
References · 40
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28 more
- 2023BridgeData V2: A Dataset for Robot Learning at Scalecited by 657
- 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 604
- 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
- 2024Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperationcited by 391
- 2023Perpetual Humanoid Control for Real-time Simulated Avatarscited by 218
- 2023Real-World Humanoid Locomotion with Reinforcement Learningcited by 187
- 2024Expressive Whole-Body Control for Humanoid Robotscited by 139
- 2024Learning Human-to-Humanoid Real-Time Whole-Body Teleoperationcited by 130
- 2022Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotioncited by 121
- 2021Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robotscited by 116
- 2023Universal Humanoid Motion Representations for Physics-Based Controlcited by 116
- 2019DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm Systemcited by 114
- 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Controlcited by 99
- 2020Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Compositioncited by 96
- 2023LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learningcited by 79
- 2022QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatarscited by 77
- 2024Learning Visuotactile Skills with Two Multifingered Handscited by 76
- 2024Agile But Safe: Learning Collision-Free High-Speed Legged Locomotioncited by 73
- 2024Humanoid Locomotion as Next Token Predictioncited by 69
- 2023Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperationcited by 54
- 2023Learning Vision-Based Bipedal Locomotion for Challenging Terraincited by 42
- 2023Teleoperation of Humanoid Robots: A Surveycited by 34
- 2023QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensorscited by 28
- 2022Neural3Points: Learning to Generate Physically Realistic Full-body Motion for Virtual Reality Userscited by 21
- 2022iCub3 Avatar System: Enabling Remote Fully-Immersive Embodiment of Humanoid Robotscited by 15
- 2023CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controllercited by 15
- 2022Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loadscited by 10
- 2018Telexistence and Teleoperation for Walking Humanoid Robotscited by 5
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Citing papers per year: 2024: 18; 2025: 87; 2026: 84.
189 papers.
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