Adaptive Task Scheduling for an Assembly Task Coworker Robot Based on Incremental Learning of Human's Motion Patterns

Jun Kinugawa, Akira Kanazawa, Shogo Arai, Kazuhiro Kosuge

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Future robots are expected to share the same workspace with humans and work in cooperation with them to improve productivity and maintain the quality of products. Considering this situation, we have developed a novel assembly task co-worker robot to support workers in their task by delivering the parts and tools to workers. Although such systems have improved work efficiency by predicting human's motion patterns, it is necessary to collect worker's data in advance and regenerate its model whenever the worker is changed. In this letter, we extend the previous system by installing an online learning algorithm and create a worker-dependent model without collecting data in advance. Trajectory prediction with high precision can be realized because of the worker-dependent model and effective utilization of the regularity of the worker's behavior. An adaptive task scheduling system based on the predicted result of the worker's behavior is proposed for improving work efficiency. Implementing the proposed algorithm, we evaluate the effectiveness of the task scheduling system by experiment.

Original languageEnglish
Article number7827110
Pages (from-to)856-863
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
Publication statusPublished - 2017 Apr


  • Cognitive human-robot interaction
  • industrial robots
  • learning and adaptive systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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