Berkeley Humanoid: A Research Platform for Learning-based Control
<|reference_start|>Berkeley Humanoid: A Research Platform for Learning-based Control: 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.<|reference_end|>
@article{liao2024berkeley,
    title={Berkeley Humanoid: A Research Platform for Learning-based Control},
    author={Qiayuan Liao, Bike Zhang, Xuanyu Huang, Xiaoyu Huang, Zhongyu Li,
  Koushil Sreenath},
    journal={arXiv preprint arXiv:2407.21781},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21781},
    primaryClass={cs.RO}
}