Berkeley Humanoid: A Research Platform for Learning-based Control
Authors: name: Qiayuan Liao, name: Bike Zhang, name: Xuanyu Huang, name: Xiaoyu Huang, name: Zhongyu Li, name: Koushil Sreenath

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.