Contact State Estimation using Machine Learning
File(s)
Accepted version
Author(s)
Jamali, Nawid
Kormushev, Petar
Caldwell, Darwin G
Type
Conference Paper
Abstract
In this paper we present an approach that uses machine learning to determine the location of a contact between a gripper and a T-bar valve based on force/torque sensor data. The robot performs an exploratory behaviour that produces distinct force/torque data for each contact location of interest: no contact, a contact aligned with the central axis of the valve, and an off-center contact. Probabilistic clustering is utilised to transform the multidimensional data into a one-dimensional sequence of symbols, which is then used to train a hidden Markov model classifier. We present the results of an experiment where the learned classifier can predict a contact location with an accuracy of 97% on an unseen dataset.
Date Issued
2013-09
Date Acceptance
2013-09-23
Citation
Proc. MTS/IEEE Intl Conf. OCEANS 2013, 2013
Publisher
IEEE
Start Page
1
End Page
4
Journal / Book Title
Proc. MTS/IEEE Intl Conf. OCEANS 2013
Copyright Statement
© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6740992
Source
OCEANS 2013
Publication Status
Published
Start Date
2013-09-23
Finish Date
2013-09-27
Coverage Spatial
San Diego, CA