Task-oriented generalization of dynamic movement primitive

Abstract

An important question in imitation learning is how to generalize a learned motion to novel situations. The motion generalization depends on a set of features which can be represented as feature vectors spanning a feature space, called query space. The purpose of generalization is to find a mapping from this query space to the motion primitive space (MP space). In this paper, we address the problem of generalization of dynamic movement primitives (DMPs) to new queries by applying locally weighted regression (LWR) with radial basis functions (RBF). Since two DMPs differ only in their non-linear part, we transform the problem of DMP generalization to a regression analysis problem. We introduce a task-oriented regression algorithm with a cost function that takes task constraints into consideration and which relies on model switching to solve the problem of poor DMP generalization when using a single regression model for the entire query space. The evaluation shows that our algorithm outperforms related approaches in the literature in terms of generalization capabilities.