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Real-time human progress estimation with online dynamic time warping for collaborative robotics
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DOI：10.3389/frobt.2025.1623884.png)
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Abstract
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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.
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