Contact state estimation using machine learning

File Description SizeFormat 
Jamali_OCEANS-2013.pdfAccepted version1.8 MBAdobe PDFView/Open
Title: Contact state estimation using machine learning
Authors: Jamali, N
Kormushev, P
Caldwell, DG
Item 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.
Issue Date: 30-Sep-2013
Date of Acceptance: 23-Sep-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.
Conference Name: OCEANS 2013
Publication Status: Published
Publisher URL:
Start Date: 2013-09-23
Finish Date: 2013-09-27
Conference Place: San Diego, CA
Appears in Collections:Dyson School of Design Engineering

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commonsx