Phase Distribution in Probabilistic Movement Primitives, Representing Time Variability for the Recognition and Reproduction of Human Movements

TL;DR

This paper introduces a method to improve probabilistic movement primitives by aligning temporal observations, enhancing recognition and reproduction of human movements in robotic applications.

Contribution

It proposes a novel temporal alignment technique for ProMPs that maximizes spatial information while maintaining smooth phase velocity, aiding movement recognition and reproduction.

Findings

Enhanced movement recognition accuracy

Improved movement reproduction quality

Effective temporal alignment in 2D reaching tasks

Abstract

Probabilistic Movement Primitives (ProMPs) are a widely used representation of movements for human-robot interaction. They also facilitate the factorization of temporal and spatial structure of movements. In this work we investigate a method to temporally align observations so that when learning ProMPs, information in the spatial structure of the observed motion is maximized while maintaining a smooth phase velocity. We apply the method on recordings of hand trajectories in a two-dimensional reaching task. A system for simultaneous recognition of movement and phase is proposed and performance of movement recognition and movement reproduction is discussed.