Berkeley Humanoid: A Research Platform for Learning-based Control
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913454163492864 |
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| author | Liao, Qiayuan Zhang, Bike Huang, Xuanyu Huang, Xiaoyu Li, Zhongyu Sreenath, Koushil |
| author_facet | Liao, Qiayuan Zhang, Bike Huang, Xuanyu Huang, Xiaoyu Li, Zhongyu Sreenath, Koushil |
| contents | We introduce Berkeley Humanoid, a reliable and low-cost mid-scale humanoid research platform for learning-based control. Our lightweight, in-house-built robot is designed specifically for learning algorithms with low simulation complexity, anthropomorphic motion, and high reliability against falls. The robot's narrow sim-to-real gap enables agile and robust locomotion across various terrains in outdoor environments, achieved with a simple reinforcement learning controller using light domain randomization. Furthermore, we demonstrate the robot traversing for hundreds of meters, walking on a steep unpaved trail, and hopping with single and double legs as a testimony to its high performance in dynamical walking. Capable of omnidirectional locomotion and withstanding large perturbations with a compact setup, our system aims for scalable, sim-to-real deployment of learning-based humanoid systems. Please check http://berkeley-humanoid.com for more details. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_21781 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Berkeley Humanoid: A Research Platform for Learning-based Control Liao, Qiayuan Zhang, Bike Huang, Xuanyu Huang, Xiaoyu Li, Zhongyu Sreenath, Koushil Robotics We introduce Berkeley Humanoid, a reliable and low-cost mid-scale humanoid research platform for learning-based control. Our lightweight, in-house-built robot is designed specifically for learning algorithms with low simulation complexity, anthropomorphic motion, and high reliability against falls. The robot's narrow sim-to-real gap enables agile and robust locomotion across various terrains in outdoor environments, achieved with a simple reinforcement learning controller using light domain randomization. Furthermore, we demonstrate the robot traversing for hundreds of meters, walking on a steep unpaved trail, and hopping with single and double legs as a testimony to its high performance in dynamical walking. Capable of omnidirectional locomotion and withstanding large perturbations with a compact setup, our system aims for scalable, sim-to-real deployment of learning-based humanoid systems. Please check http://berkeley-humanoid.com for more details. |
| title | Berkeley Humanoid: A Research Platform for Learning-based Control |
| topic | Robotics |
| url | https://arxiv.org/abs/2407.21781 |