Qbot: Quadrupedal Ambulation via Reinforcement Learning

Abstract

Ambulation is a valuable form of locomotion for robots which must operate in spaces designed for human foot traffic or over uneven terrain. However, traditional approaches to robotic ambulation are laborious to implement. Recent advances in deep reinforcement learning have made it a promising alternative, but previous attempts have relied on detailed physics engine modeling for training in simulation. This project has developed a program which can teach a quadrupedal robot how to walk in real time regardless of its dimensions or configuration. The strategy presented in this project can be applied to larger, more robust quadrupeds which might serve some practical purpose. Additionally, a new reinforcement learning algorithm was discovered in the course of this research which may find applications across a wide variety of reinforcement learning problems.