Programming robots to help them perform human-specific tasks with Reinforcement and Imitation Learning algorithms.

Robots are trained in virtual environments created in Unity using Imitation Learning from a human motion capture data. During reinforcement we used a Proximal Policy Optimization to allow robot to come up with most efficient and accurate way of performing a particular task. Later on trained neural network is reinforced by couple of minutes training in a real environment.