Dynamic Interaction Probabilistic Movement Primitives

TL;DR

This paper enhances human-robot collaboration by developing a dynamic prediction framework that updates robot motions in real-time based on continuous human motion observations, improving responsiveness and accuracy.

Contribution

It reformulates Interaction Movement Primitives to incorporate dynamic human observations with phase estimation, enabling continuous updates of robot motions during collaboration.

Findings

Produces smooth, anticipatory robot motions

Increases prediction accuracy and responsiveness

Supports real-time human-robot interaction

Abstract

Human-robot collaboration is on the rise. Robots need to increasingly improve the efficiency and smoothness with which they assist humans by properly anticipating a human's intention. To do so, prediction models need to increase their accuracy and responsiveness. This work builds on top of Interaction Movement Primitives with phase estimation and re-formulates the framework to use dynamic human-motion observations which constantly update anticipatory motions. The original framework only considers a single fixed-duration static human observation which is used to perform only one anticipatory motion. Dynamic observations, with built-in phase estimation, yield a series of updated robot motion distributions. Co-activation is performed between the existing and newest most probably robot motion distribution. This results in smooth anticipatory robot motions that are highly accurate and with enhanced responsiveness.