The method for constructing a
robot expression function of a VEM-Token world model does not use a traditional NLP-Token method, but establishes a VEM-fe expression
function model, so that the
robot can directly understand emotions and learn expressions, synthesize and align multiple beat expression vectors into a synthesized expression, eliminate expression conflicts between multiple expression vectors, smoothly transition the synthesized expression from the current beat to the next beat, and use a
physical space robot or a virtual digital robot to learn, understand, and display facial expressions and dialogue expressions in social interactions. According to the consistency, simplicity, fuzziness, and compensation of the discovered expressions, through eye, mouth, head, and dialogue micro-expression
simulation, real-
time expression, and dialogue expression spectrum,
supervised learning and
reinforcement learning training are used to obtain a direct social and industry
expression library, so as to design robots for government public services, hotel front desks, restaurant front desks,
bank hall consultations, shopping mall and
specialty store guides, and the like, and realize one-to-one and one-to-many dialogues with customers.