A method for calibrating a physics engine of a virtual world simulator for learning of a deep learning-based device is provided. The method includes steps of (a) if virtual current frame information corresponding to a virtual current state in virtual environment is acquired, (i) transmitting, by a calibrating device, the virtual current frame information to the deep learning-based device to output virtual action information, (ii) transmitting, by the calibrating device, the virtual current frame information and the virtual action information to the physics engine to output virtual next frame information corresponding to the virtual current frame information and the virtual action information, and (iii) transmitting, by the calibrating device, the virtual current frame information and the virtual action information to a real state network learned to output predicted next frame information in response to action in a real environment to output predicted real next frame information; and (b) optimizing the previous calibrated parameters to generate current calibrated parameters.
METHOD AND DEVICE FOR CALIBRATING PHYSICS ENGINE OF VIRTUAL WORLD SIMULATOR TO BE USED FOR LEARNING OF DEEP LEARNING-BASED DEVICE AND A LEARNING METHOD AND LEARNING DEVICE FOR REAL STATE NETWORK USED THEREFOR