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- 1University of Padua, Padua, Italy
- 2Mitsubishi Electric Research Laboratories, Cambridge, MA, United States
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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.