The design of LEO: A 2D bipedal walking robot for online autonomous Reinforcement Learning

Abstract

Real robots demonstrating online Reinforcement Learning (RL) to learn new tasks are hard to find. The specific properties and limitations of real robots have a large impact on their suitability for RL experiments. In this work, we derive the main hardware and software requirements that a RL robot should fulfill, and present our biped robot LEO that was specifically designed to meet these requirements. We verify its aptitude in autonomous walking experiments using a pre-programmed controller. Although there is room for improvement in the design, the robot was able to walk, fall and stand up without human intervention for 8 hours, during which it made over 43; 000 footsteps.