Learning of motor skills based on grossness and fineness of movements in daily-life tasks

Abstract

In this paper, we propose a novel method for learning motor skills based on grossness and fineness of movements involved in daily-life tasks. Grossness and fineness depend on the degrees of complexity (i.e., linear combinations between basis vectors) and repeatability (i.e., repeat accuracies between multiple trials) of such movements. In such a daily-life task, a robot's movements are usually related to a task-relevant object. Therefore, the complexity and the repeatability should be acquired from datasets that include the spatial and temporal relationships between a robot and a task-relevant object. To measure the degree of complexity, correlations are first obtained from each data by canonical correlation analysis. To measure the degree of repeatability, variations are then obtained from covariances between datasets acquired by multiple trials. The grossness and fineness are finally acquired by combining the correlations and the variations. To learn a motor skill, a Gaussian Mixture Model (GMM) is estimated using well-known methods as Principal Component Analysis (PCA), k-means, Bayesian Information Criterion (BIC), and Expectation-Maximization (EM) algorithms. First, initial parameters of a GMM are estimated by weighting a conventional k-means algorithm with the grossness and fineness. Based on PCA, BIC, and EM algorithms, the GMM is then estimated using the initial parameters and a robot's motion trajectories. To validate our proposed methods, the GMM is evaluated in terms of reproduction and recognition using a robot arm that performs two daily-life tasks: cookie-decorating and constrained-delivering tasks.