Real-time human progress estimation with online dynamic time warping for collaborative robotics.

ABSTRACT: Real-time estimation of human action progress is critical for seamless human-robot collaboration yet remains underexplored. With this paper we propose the first real-time application of Open-end Soft-DTW (OS-DTW_EU) and introduce OS-DTW_WP, a novel DTW variant that integrates a Windowed-Pearson distance to effectively capture local correlations. This method is embedded in our Proactive Assistance through action-Completion Estimation (PACE) framework, which leverages reinforcement learning to synchronize robotic assistance with human actions by estimating action completion percentages. Experiments on a chair assembly task demonstrate OS-DTW_WP's superiority in capturing local motion patterns and OS-DTW_EU's efficacy in tasks presenting consistent abso

SUBMITTER: De Lazzari D 

PROVIDER: S-EPMC12712710 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature