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ORIGINAL RESEARCH article

Front. Robot. AI

Sec. Robotic Control Systems

Volume 12 - 2025 | doi: 10.3389/frobt.2025.1623884

This article is part of the Research TopicSupervised Autonomy: How to Shape Human-Robot Interaction from the Body to the BrainView all articles

Real-time Human Progress Estimation with Online Dynamic Time Warping for Collaborative Robotics

Provisionally accepted
Davide De LazzariDavide De Lazzari1,2*Matteo TerreranMatteo Terreran1Giulio GiacomuzzoGiulio Giacomuzzo1Siddarth JainSiddarth Jain2Pietro FalcoPietro Falco1Ruggero CarliRuggero Carli1Stefano GhidoniStefano Ghidoni1Diego RomeresDiego Romeres2*
  • 1University of Padua, Padua, Italy
  • 2Mitsubishi Electric Research Laboratories, Cambridge, MA, United States

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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-DTWEU) and introduce OS-DTWWP, 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-DTWWP's superiority in capturing local motion patterns and OS-DTWEU'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.

Keywords: Open-end Dynamic Time Warping, Human Action Progress Estimation, Human Action Completion Time Prediction, human-robot interaction, Collaborative assembly, Real-time monitoring, reinforcement learning, Sliding window cross-correlation

Received: 06 May 2025; Accepted: 02 Oct 2025.

Copyright: © 2025 De Lazzari, Terreran, Giacomuzzo, Jain, Falco, Carli, Ghidoni and Romeres. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Davide De Lazzari, dadidelazzari@gmail.com
Diego Romeres, romeres@merl.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material