Abstract:

Real-time estimation of human action progress is critical for seamless human-robot collaboration3 yet remains underexplored. With this paper we propose the first real-time application of Open-4 end Soft-DTW (OS-DTWEU) and introduce OS-DTWWP, a novel DTW variant that integrates a5 Windowed-Pearson distance to effectively capture local correlations. This method is embedded6 in our Proactive Assistance through action-Completion Estimation (PACE) framework, which7 leverages reinforcement learning to synchronize robotic assistance with human actions by8 estimating action completion percentages. Experiments on a chair assembly task demonstrate9 OS-DTWWP’s superiority in capturing local motion patterns and OS-DTWEU’s efficacy in tasks10 presenting consistent absolute positions. Moreover we validate the PACE framework through11 user studies involving 12 participants, showing significant improvements in interaction fluency,12 reduced waiting times, and positive user feedback compared to traditional methods.