Disclosed are a robot motor-skill learning method and system, said method comprising: obtaining a human drag demonstration data sample set; performing dimensionality reduction on said data sample set on the basis of principal component analysis; establishing variable constraint conditions in a latent space and, in combination with said variable constraint conditions, filtering the data sample set after dimensionality reduction to generate a latent space data set; using a Gaussian mixture model with Gaussian mixture regression to perform modeling and learning of said latent space data set, to output a robot motion-control training model; performing prediction on said robot motion-control training model on the basis of a recurrent neural network to solve for the optimal solution of the model, and converting the model optimal solution into an actual control variable of the robot. In the embodiments of the present invention, autonomous learning of robot motor skills can be achieved by means of using a small amount of human demonstration data while taking into account the inherent constraints of the robot body, effectively improving the generalization ability and programming efficiency of an algorithm.
ROBOT MOTOR-SKILL LEARNING METHOD AND SYSTEM