CDPMM-DMP: Conditional Dirichlet Process Mixture Model-Based Dynamic Movement Primitives

Movement Primitives (MPs) are compact generators for representation and generalization of modular movements, which are usually used to implement learning from demonstration tasks in robotics. Existing works on MPs mostly utilize combinations of basis functions to represent diverse movements, whether employing probabilistic or dynamic approaches. However, applying these approaches requires manual specification of hyperparameters related to basis functions, resulting in inconvenience and a reliance on specific expertise. In this paper, we develop a Conditional Dirichlet Process Mixture Model-based Dynamic Movement Primitive (CDPMM-DMP) to achieve a non-parametric improvement for the Dynamic Movement Primitive (DMP). First, inspired by Bayesian nonparametric theory, we explore the use of the Dirichlet Process Mixture Model (DPMM) to replace the original radial basis functions in the DMP, and construct the required training set from demonstrations. Then, we study the output generation mechanism driven by the DPMM, particularly by employing conditional sampling to avoid the anomalous outputs caused by direct sampling from the DPMM. Finally, we provide analyses of the various properties brought by our nonparametric transformation of DMP. The analyses and validation results show that the proposed CDPMM-DMP can significantly reduce the parameter tuning burden in usage with its nonparametric learning property. Besides, our method still retains the inherent properties of DMP, while also incorporating some properties of probabilistic MPs, such as multi-sample learning and co-activation.