ViolinBot: A Framework for Imitation Learning of Violin Bowing Using Fuzzy Logic and PCA

In this paper, an imitation learning framework is innovatively developed for robot skills learning in violin bowing. The introduction of Dynamic Movement Primitives (DMPs) to model motion addresses issues such as the uncertainty in changing string angles, which traditional methods like physical measurements are highly error-prone and non-generalizable. Alternatively, Conventional rule-based methods often fail to precisely mimic the complex playing techniques of humans, especially in terms of musicality and emotional expression. To address this, we propose a new model named Fuzzy and PCADynamic Movement Primitive (FP-DMP), built upon Type-2 Fuzzy Models and Principal Component Analysis (PCA). This model utilizes variables derived from PCA (bowing plane angles) as inputs for the membership function and employs fuzzy kmeans clustering to identify the angle of the violin bowing plane, independent of physical measurements or positional information. Additionally, we have developed a professional-level musical performance behavior database through a Task-Parameterized Gaussian Mixture Model (TP-GMM). Biomimetic experiments prove that our FP-DMP model can control robots for highprecision violin performances. The core technology, FP-DMP, combines Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA) for efficient trajectory clustering based on bowing angles, uses type-2 fuzzy K-means for nuanced differentiation of violin string trajectories, and innovatively adapts the force term in DMP to account for the non-linear friction encountered in violin playing. This not only advances imitation learning in complex performance tasks for robots but also opens new research avenues for imitation learning in other highly specialized and technically challenging fields.