Intuitive human skill reconstruction for compliance control

Samuel Okodi, Xin Jiang, Satoko Abiko, Atsushi Konno, Masaru Uchiyama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents a robust and efficient method of generating manipulation motion skill for non-force-feedback high speed constrained compliant robot motion. Using a non-structured teaching environment, the inherent task in the captured demonstration force and position data is estimated and reconstructed from three sets of complimentary models, including analytical mathematical modelling, empirical modelling and human skill demonstration modelling. The approach addresses task specification accuracy deficiencies, and involves outward interface simplifications, with embedded rigorous analytical methodologies that enable users to realise complex and robust constrained compliant robot motion without dealing with the low level motion generation aspects. Function based task representation supports an intuitive approach to generate robust constrained motion by skill superimposition, as exemplified by peg-in-hole with crank turning.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
Pages5576-5581
Number of pages6
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 IEEE International Conference on Robotics and Automation, ICRA 2010 - Anchorage, AK, United States
Duration: 2010 May 32010 May 7

Other

Other2010 IEEE International Conference on Robotics and Automation, ICRA 2010
CountryUnited States
CityAnchorage, AK
Period10/5/310/5/7

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Okodi, S., Jiang, X., Abiko, S., Konno, A., & Uchiyama, M. (2010). Intuitive human skill reconstruction for compliance control. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 5576-5581). [5509896] https://doi.org/10.1109/ROBOT.2010.5509896