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Related Experiment Video

Updated: Jan 8, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

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

Davide De Lazzari¹, Matteo Terreran¹, Giulio Giacomuzzo¹

¹Department of Information Engineering, University of Padua, Padua, Italy.

Frontiers in Robotics and AI

|December 22, 2025

Summary

This summary is machine-generated.

This study introduces novel Dynamic Time Warping (DTW) methods for real-time human action progress estimation, enhancing human-robot collaboration. The PACE framework improved interaction fluency and reduced waiting times in user studies.

Keywords:

collaborative assembly human action completion time prediction human action progress estimation human-robot interaction open-end dynamic time warping real-time monitoring reinforcement learning sliding window cross-correlation

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Area of Science:

Robotics
Human-Computer Interaction
Machine Learning

Background:

Real-time human action progress estimation is crucial for effective human-robot collaboration.
Current methods for action progress estimation are underexplored, limiting seamless collaboration.

Purpose of the Study:

To propose the first real-time application of Open-end Soft-DTW (OS-DTW_EU).
To introduce OS-DTW_WP, a novel DTW variant for capturing local correlations.
To develop the Proactive Assistance through action-Completion Estimation (PACE) framework for synchronized robotic assistance.

Main Methods:

Implemented real-time OS-DTW_EU and introduced OS-DTW_WP with Windowed-Pearson distance.
Integrated these methods into the PACE framework using reinforcement learning for action completion estimation.
Conducted experiments on a chair assembly task and user studies with 12 participants.

Main Results:

OS-DTW_WP demonstrated superiority in capturing local motion patterns.
OS-DTW_EU proved effective for tasks with consistent absolute positions.
The PACE framework significantly improved interaction fluency, reduced waiting times, and received positive user feedback.

Conclusions:

The proposed OS-DTW variants and PACE framework offer a significant advancement in real-time human action progress estimation.
This research enables more fluid and efficient human-robot collaboration.
The findings highlight the potential for adaptive robotic assistance in complex tasks.