Human skill capture: A Hidden Markov Model of force and torque data in peg-in-a-hole assembly process

Y. C. Zhao, A. Al-Yacoub, Y. M. Goh, L. Justham, N. Lohse, M. R. Jackson

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

Abstract

A new model has been constructed to generalise the force and torque information during a manual peg-in-a-hole (PiH) assembly process. The paper uses Hidden Markov Model analysis to interpret the state topology (transition probability) and observations (force/torque signal) in the manipulation task. The task can be recognised as several discrete states that reflect the intrinsic nature of the process. Since the whole manipulation process happens so fast, even the operator themselves cannot articulate the exact states. Those are tacit skills which are difficult to extract using human factors methodologies. In order to programme a robot to complete tasks at skill level, numerical representation of the sub-goals are necessary. Therefore, those recognised 'hidden' states become valuable when a detail explanation of the task is needed and when a robot controller needs to change its behaviour in different states. The Gaussian Mixture model (GMM) is used as the initial guess of observations distribution. Then a Hidden Markov Model is used to encode the state (sub-goal) topology and observation density associated with those sub-goals. The Viterbi algorithm is then applied for the model-based analysis of the force and torque signal and the classification into sub-goals. The Baum-Welch algorithm is used for training and to estimate the most likely model parameters. In addition to generic states recognition, the proposed method also enhances our understanding of the skill based performances in manual tasks.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages655-660
Number of pages6
ISBN (Electronic)9781509018970
DOIs
Publication statusPublished - 6 Feb 2017
Event2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary
Duration: 9 Oct 201612 Oct 2016

Publication series

Name2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

Conference

Conference2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
Country/TerritoryHungary
CityBudapest
Period9/10/1612/10/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Control and Optimization
  • Human-Computer Interaction

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