伯克利人形機器人：一個基於學習控制的研究平台

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

我們介紹了伯克利人形機器人，這是一個可靠且低成本的中型人形機器人研究平台，用於基於學習的控制。我們輕量級的自製機器人專門設計用於低模擬複雜度、類人運動和高抗跌落可靠性的學習算法。該機器人窄小的模擬至真實差距實現了在戶外環境中通過簡單的強化學習控制器使用輕量級領域隨機化來實現靈活且穩健的運動，可以在各種地形上進行機動。此外，我們展示了機器人在數百米範圍內行走，走在陡峭的未鋪設小徑上，以及單腿和雙腿跳躍，證明了其在動態行走方面的高性能。我們的系統具有全方位運動能力，並能夠承受大的干擾，具有緊湊的配置，旨在實現基於學習的人形系統的可擴展模擬至真實部署。請查看http://berkeley-humanoid.com以獲取更多詳細信息。

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.