Transfer Learning-enabled Action Recognition for Human-robot Collaborative Assembly

Shufei Li, Junming Fan, Pai Zheng (Corresponding Author), Lihui Wang

Research output: Journal article publicationConference articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Human-robot collaboration (HRC) is critical to today's tendency towards high-flexible assembly in manufacturing. Human action recognition, as one of the core prerequisites for HRC, enables industrial robots to understand human intentions and to execute planning adaptively. However, existing deep learning-based action recognition methods rely heavily on a huge amount of annotation data, which may not be effective or realistic in practice. Therefore, a transfer learning-enabled action recognition approach is proposed in this research to facilitate robot reactive control in HRC assembly. Meanwhile, a decision-making mechanism for robotic planning is introduced as well. Lastly, the proposed approach is evaluated in an aircraft bracket assembly scenario to reveal its significance.

Original languageEnglish
Pages (from-to)1795-1800
Number of pages6
JournalProcedia CIRP
Volume104
DOIs
Publication statusPublished - 22 Sep 2021
Event54th CIRP Conference on Manufacturing Ssystems, CMS 2021 - Patras, Greece
Duration: 22 Sep 202124 Sep 2021

Keywords

  • action recognition
  • domain adaptation
  • human-robot collaboration assembly
  • Transfer learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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