伯克利人形机器人：基于学习控制的研究平台

Berkeley Humanoid: A Research Platform for Learning-based Control

July 31, 2024

作者: Qiayuan Liao, Bike Zhang, Xuanyu Huang, Xiaoyu Huang, Zhongyu Li, Koushil Sreenath

cs.AI

摘要

我们介绍了伯克利人形机器人，这是一个可靠且低成本的中等规模人形机器人研究平台，用于基于学习的控制。我们自行设计的轻量级机器人专为具有低仿真复杂性、类人动作和高抗摔倒可靠性的学习算法而设计。该机器人窄小的仿真到真实差距实现了在户外环境中通过简单的强化学习控制器和轻量级领域随机化实现敏捷且稳健的跨越各种地形的步行。此外，我们展示了机器人在数百米范围内行走，走在陡峭的未铺设小径上，并以单腿和双腿跳跃，证明了其在动态步行中的高性能。我们的系统能够实现全向运动并以紧凑的设置承受大幅干扰，旨在实现基于学习的人形系统的可扩展仿真到真实部署。请访问http://berkeley-humanoid.com了解更多详情。

English

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.