Abstract
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 this http URL for more details.
Abstract (translated)
我们推出了Berkeley Humanoid，这是一个可靠且成本低的中规模人形研究平台，用于学习控制。我们的轻量级、由内部构建的机器人专门设计用于具有低仿真复杂度、人形运动和高度可靠性应对跌落的学习算法。机器人狭窄的模拟与现实之间的差距使得它可以灵活地应对各种户外环境中的地形，通过使用简单的强化学习控制器实现光域随机化。此外，我们还展示了机器人行进数百米，走在陡峭的不平路上，并双脚跳跃作为其高动态步行性能的证明。能够实现全方位移动，且具有紧凑的设置，我们的系统旨在实现基于学习的对人形系统的可扩展、模拟与实时的部署。更多详情，请查看此链接：
URL
https://arxiv.org/abs/2407.21781
