Hierarchical World Models as Visual Whole-Body Humanoid Controllers
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913837465206784 |
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| author | Hansen, Nicklas S V, Jyothir Sobal, Vlad LeCun, Yann Wang, Xiaolong Su, Hao |
| author_facet | Hansen, Nicklas S V, Jyothir Sobal, Vlad LeCun, Yann Wang, Xiaolong Su, Hao |
| contents | Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visual observations further exacerbates this difficulty. In this work, we explore highly data-driven approaches to visual whole-body humanoid control based on reinforcement learning, without any simplifying assumptions, reward design, or skill primitives. Specifically, we propose a hierarchical world model in which a high-level agent generates commands based on visual observations for a low-level agent to execute, both of which are trained with rewards. Our approach produces highly performant control policies in 8 tasks with a simulated 56-DoF humanoid, while synthesizing motions that are broadly preferred by humans. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18418 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchical World Models as Visual Whole-Body Humanoid Controllers Hansen, Nicklas S V, Jyothir Sobal, Vlad LeCun, Yann Wang, Xiaolong Su, Hao Machine Learning Computer Vision and Pattern Recognition Robotics Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visual observations further exacerbates this difficulty. In this work, we explore highly data-driven approaches to visual whole-body humanoid control based on reinforcement learning, without any simplifying assumptions, reward design, or skill primitives. Specifically, we propose a hierarchical world model in which a high-level agent generates commands based on visual observations for a low-level agent to execute, both of which are trained with rewards. Our approach produces highly performant control policies in 8 tasks with a simulated 56-DoF humanoid, while synthesizing motions that are broadly preferred by humans. |
| title | Hierarchical World Models as Visual Whole-Body Humanoid Controllers |
| topic | Machine Learning Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2405.18418 |