Transferring robot manipulation policies from simulation to real-world

Robots are increasingly becoming part of our lives, but despite their impressive repertoire of tasks, many of them will fail to adapt when presented to new and unfamiliar environments. Before robots can realise their full potential in everyday life, they need the ability to manipulate the changing world around them. Recent trends to solve this problem have seen a shift to end-to-end solutions using deep reinforcement learning policies from visual input. However, these approaches are run on real-world robotic platforms which often require human interaction, expensive equipment, and long training times. We aim to explore the feasibility of using life-like simulations to train robot manipulations tasks that are then directly map to real-world hardware