Learning to walk: Reinforcement Learning for robotic locomotion

Locomotion is a fundamental skill necessary for any autonomous robot or system which wants to effect significant changes within it's environment. Only the most simplistic production line robotics can operate entirely from a fixed location. However, locomotion is also one of the most varied and
challenging skills to learn in a robust and generalisable way. In nature there are countless examples of different locomotion strategies, and they almost universally require a huge time investment to master. Attempts to go beyond wheeled locomotion in robotics tend to be very brittle. The robot often cannot adapt when the material properties of the terrain are unexpected, or the surface is irregular. This project will aim to address this gap by learning robust and adaptive walking strategies through trial and error reinforcement learning. The project will explore a range of exotic locomotion strategies,
from hybrid wheel-legs to starfish-like hexapod robots. In these scenarios the robot will attempt to estimate the properties of the surface it is moving across based on both perception, and it's own locomotion behaviours. It will then use this as conditioning information to inform the following locomotion strategy. The project will specifically explore symbolic-RL as a high efficiency control loop for rapid locomotion, and may also touch on areas of automated robotic design, with regards to learning the optimal locomotion hardware.