Teaching Quadruped to Walk Using Fault Adaptive Deep Reinforcement Learning Algorithm

Abstract

Reinforcement learning holds the promise of enabling autonomous robots to learn behavioural skills with minimal human intervention. Reinforcement learning in robotics allows designing an algorithm that makes a robot learn a specific task by itself and arrive at an optimal policy that accomplishes its task without being specifically programmed to do so. This paper demonstrates the approach of using Deep reinforcement Learning in Teaching a quadruped robot how to walk, without programming it specifically to do so, and how to utilize the ability of self-learning robots in fault adaptive control algorithm based on reinforcement learning to teach the robot to adapt to faults and accomplish its mission.