Entertainment type robots based on machine learning and game teaching mode applied in dance action planning of art teaching

Abstract

We studied the use of machine learning models for training dance action models, collected dance action data, annotated and classified it, and established a training set for dance action models. We trained the training set to learn the feature representation and pattern recognition capabilities of dance actions. Through training and tuning the model, a model that can accurately recognize and generate dance movements was obtained. Evaluate the similarity between two dance movements and select the appropriate dance movements to form a smooth dance sequence. A planning algorithm was designed based on the kinematic and dynamic characteristics of robots to generate dance action paths suitable for the robot’s body conditions. Considering factors such as joint limitations, body stability, and smooth movement of the robot, generate a reasonable dance motion path while ensuring safety. Through on-site testing and data analysis of the system, it has been verified that it can effectively generate diverse and expressive dance movements, bringing a unique viewing experience to entertainment venues.