Real-time human progress estimation with online dynamic time warping for collaborative robotics
Davide De Lazzari, Matteo Terreran, Giulio Giacomuzzo, Siddarth Jain, Pietro Falco, Ruggero Carli, Stefano Ghidoni, Diego Romeres · Frontiers in Robotics and AI · 2025
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 absolute positions. Moreover we validate the PACE framework through user studies involving 12 participants, showing significant improvements in interaction fluency, reduced waiting times, and positive user feedback compared to traditional methods.