PACE: Proactive Assistance in Human-Robot Collaboration Through Action-Completion Estimation

Davide De Lazzari; Matteo Terreran; Giulio Giacomuzzo; Siddarth Jain; Pietro Falco; Ruggero Carli; Diego Romeres

ICRA 20253 citations

PACE: Proactive Assistance in Human-Robot Collaboration Through Action-Completion Estimation

Davide De Lazzari, Matteo Terreran, Giulio Giacomuzzo, Siddarth Jain, Pietro Falco, Ruggero Carli, Diego Romeres

Abstract

This paper introduces the Proactive Assistance through action-Completion Estimation (PACE) framework, designed to enhance human-robot collaboration through real-time monitoring of human progress. PACE incorporates a novel method that combines Dynamic Time Warping (DTW) with correlation analysis to track human task progression from hand movements. PACE trains a reinforcement learning policy from limited demonstrations to generate a proactive assistance policy that synchronizes robotic actions with human activities, minimizing idle time and enhancing collaboration efficiency. We validate the framework through user studies involving 12 participants, showing significant improvements in interaction fluency, reduced waiting times, and positive user feedback compared to traditional methods.

BibTeX

@inproceedings{icra2025_paceproactiveass,
  title = {PACE: Proactive Assistance in Human-Robot Collaboration Through Action-Completion Estimation},
  author = {Davide De Lazzari and Matteo Terreran and Giulio Giacomuzzo and Siddarth Jain and Pietro Falco and Ruggero Carli and Diego Romeres},
  booktitle = {ICRA 2025},
  year = {2025}
}