Intelligent Drumming Robot for Human Interaction

Autor: Yukun Su, Zecong Li, Jinhui Zhu, Yang Yi, Linghui Sui
Rok vydání: 2020
Předmět:
Zdroj: 2020 International Symposium on Autonomous Systems (ISAS).
Popis: The advances in information technology have witnessed great progress on robot technologies in various domains nowadays. These new technologies enable robots to be used in industries, agriculture, education, and all aspects of our lives. In the field of robotic musicians, the core challenges are to teach robots to play musical instruments. Previous studies rely on manual closed-loop adjustments or pre-designed feature functions that rely on the expertise. However, they ignore the mechanisms to learn from humans and lacks corresponding science education and art teaching functions. In addition, most of the playing robots are created by the artificial pre-set audio action sequence. When facing new data features, it is difficult to compute. In order to solve these problems, this paper presents an intelligent drumming robot, based on machine learning computing technologies. The system consists of a user dashboard module, a computing module, and a data-oriented execution module. The drumming robot can be used for drum teaching and provides a new way of learning for the teaching of drums. The results of this study show that the combination of machine learning physical devices can be used to enhance the performance of drumming robot systems, allowing humans to interact with robots and enjoy a variety of smart applications and services.
Databáze: OpenAIRE