From simulation to reality: migration of humanoid robot control

Physical simulation is an effective and practical method, to apply to the study and exploration of real world problems. However, simulation can offer valuable results for robotics only in close connection to real robots. In this thesis, we investigated how to create a mechanism that provides a smooth gradient to transfer humanoid robot control from simulated robot to real robot. We developed a framework for running robots both in real and simulated settings; and evaluated a humanoid robot simulator at a conceptual model level and results level by conducting experiments. Then, we improved the simulator by adding missing models and optimizing parameters with Evolutionary Algorithms. Finally, we developed motions in the simulations, with the help of Machine Learning, and transferred them to real robots successfully. As a result, a robot team can play soccer using identical controls in both the simulation and real RoboCup leagues. This constitutes a close connection between the communities working with simulated and real robots.