Teaching Humanoid Robots to Assist Humans for Collaborative Tasks

Julia Rodano, Omar Obidat, Jesse Parron, Rui Li, Michelle Zhu, Weitian Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As technology has advanced, society has witnessed and participated in the creation of robots that can walk, talk, and recognize speech. To facilitate communication and collaboration between humans and humanoid robots, we develop a teaching-learning framework for human beings to teach humanoid robots to complete object identification and operation tasks. The robots learn from their human partners based on the transfer learning approach and can assist humans using their learned knowledge. Experimental results and evaluations suggest the success and efficiency of the developed approach in smart service contexts for human-robot partnerships. The future work of this study is also discussed.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE International Conference on Smart Computing, SMARTCOMP 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages344-348
Number of pages5
ISBN (Electronic)9798350322811
DOIs
StatePublished - 2023
Event9th IEEE International Conference on Smart Computing, SMARTCOMP 2023 - Nashville, United States
Duration: 26 Jun 202329 Jun 2023

Publication series

NameProceedings - 2023 IEEE International Conference on Smart Computing, SMARTCOMP 2023

Conference

Conference9th IEEE International Conference on Smart Computing, SMARTCOMP 2023
Country/TerritoryUnited States
CityNashville
Period26/06/2329/06/23

Keywords

  • Humanoid robots
  • human-robot collaboration
  • smart service systems
  • transfer learning
  • vision system

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