Dual-Filtering for On-Line Simultaneously Estimate Weights and Phase Parameter of Probabilistic Movement Primitives for Human-Robot Collaboration

The Probabilistic Movement Primitives (ProMPs) is an essential issue and framework for robotics Learning from Demonstration (LfD). It has been successfully applied to the robotics field in tasks such as skill acquisition and Human-Robot Collaboration (HRC). In this paper, we focus on its adaptability in the HRC scenario, in which the adaptability of the ProMPs allows the robot to predict the future movement of its human partner and plan its movement accordingly, given the observed human movement. Most of the existing works about the application of the ProMPs in HRC either only focus on the estimation of the weights on-line and lack the estimation of the phase parameter or merely depend on the prior distribution of the phase parameter. As a result, these methods can lead to a misinterpretation of the basis matrix when the divergence between the prior distribution and the posterior distribution of the phase parameter becomes large, resulting in a divergence of the estimation of the weights. In this paper, we propose a Dual-Filtering method for the ProMPs, which is able to simultaneously on-line estimate the weights and phase parameter for the ProMPs. The preliminary experimental result demonstrates the proposed method is able to provide better prediction performance and more accurate estimation of the phase parameter in comparison with the previous works. ? 2021 IEEE.