TY - GEN
T1 - Data-driven robot gait modeling via symbolic time series analysis
AU - Seto, Yusuke
AU - Takahashi, Noboru
AU - Jha, Devesh K.
AU - Virani, Nurali
AU - Ray, Asok
N1 - Funding Information:
This work has been supported in part by the U.S. Air Force Office of Scientific Research under Grant No. FA9550-15-1-0400 and by the U.S. Army Research Laboratory under Grant No. W911NF-14-2-0068. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies
Publisher Copyright:
© 2016 American Automatic Control Council (AACC).
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2016/7/28
Y1 - 2016/7/28
N2 - 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.
AB - 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.
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U2 - 10.1109/ACC.2016.7525522
DO - 10.1109/ACC.2016.7525522
M3 - Conference contribution
AN - SCOPUS:84992065220
T3 - Proceedings of the American Control Conference
SP - 3904
EP - 3909
BT - 2016 American Control Conference, ACC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 American Control Conference, ACC 2016
Y2 - 6 July 2016 through 8 July 2016
ER -