Training a Legged Robot to Walk Using Machine Learning and Trajectory Control for High Positional Accuracy

Abstract

Legged robots are a class of biologically inspired robots that use articulated leg mechanisms for locomotion. Legged motion is very complex and requires specialized actuation mechanisms and complicated motion control systems to operate. Traditional legged robots were controlled by purely physics-based, however, recent developments of artificial intelligence (AI), and machine learning (ML) techniques have opened new opportunities to train locomotion skills in a legged robot in a much more efficient way than the traditional physics-based controllers. In this chapter, the authors study how machine learning techniques are used to train quadruped robots in basic locomotion skills, evaluate training accuracy, training speed, and also discussed performance, simulation environment, trajectory control, and how the authors achieved accurate tracking of trajectories. Furthermore, this chapter delves into the details of the actual quadruped robot that the authors built to evaluate locomotion policies and some challenges that were faced in building the real robot.