Berkeley Humanoid: A Research Platform for Learning-based Control

TL;DR

Berkeley Humanoid is a low-cost, reliable research platform designed for learning-based control, enabling robust outdoor locomotion and dynamic behaviors with minimal sim-to-real gap.

Contribution

We present a new humanoid robot platform optimized for learning algorithms, featuring high reliability, low simulation complexity, and demonstrated capabilities in diverse outdoor terrains.

Findings

Successful outdoor locomotion over varied terrains

Robust walking and hopping with minimal simulation gap

High performance in dynamic and perturbation-rich environments

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 http://berkeley-humanoid.com for more details.