Data-driven robot gait modeling via symbolic time series analysis

Yusuke Seto, Noboru Takahashi, Devesh K. Jha, Nurali Virani, Asok Ray

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

4 Scopus citations


This paper addresses data-driven mode modeling and Bayesian mode estimation in hidden-mode hybrid systems (HMHS). For experimental validation in a laboratory setting, an HMHS is built upon a six-legged T-hex robot that makes use of a library of gaits (i.e., the modes of walking) to perform different motion maneuvers. To accurately predict the behavior of the robot, it is important to first infer the gait being used by the robot. The walking robot's motion behavior can then be modeled as a transition system based on the pattern of switching among these gaits. In this paper, a symbolic time-series-based statistical learning method has been adopted to construct the generative models of the gaits. Efficacy of the proposed algorithm is demonstrated by laboratory experimentation to model and then infer the hidden dynamics of different gaits for the T-hex walking robot.

Original languageEnglish (US)
Title of host publication2016 American Control Conference, ACC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781467386821
StatePublished - Jul 28 2016
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: Jul 6 2016Jul 8 2016

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2016 American Control Conference, ACC 2016
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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