Model-based Reinforcement Learning for Sim-to-Real Transfer in Robotics using HTM neural networks

In this work we propose a novel approach based on model-based Reinforcement Learning (RL) for the sim-to-real transfer of industrial robots. Specifically, we propose to employ a recently developed kinematics-informed, modular neural network serving as a learnable environment model within the world model framework. Using the kinematics-informed model, training of the world model is made more efficient resulting in faster training. Furthermore, the approach allows to train industrial robots on specific tasks solely within the simulation of the system thereby saving time and energy-consumption. Using simulations ensures safe and controlled training implementation and allows for parallelization to increase training speed. We conduct various experiments which underline the effectiveness of the proposed method. We show that training the RL algorithm solely within the simulation, results in a hundred percent task completion rate in both simulation and real world experiments.