Robot-Object Contact Perception using Symbolic Temporal Pattern Learning
File(s)
Accepted version
Author(s)
Jamali, Nawid
Kormushev, Petar
Caldwell, Darwin G
Type
Conference Paper
Abstract
This paper investigates application of machine learning to the problem of contact perception between a robots gripper and an object. The input data comprises a multidimensional time-series produced by a force/torque sensor at the robots wrist, the robots proprioceptive information, namely, the position of the end-effector, as well as the robots control command. These data are used to train a hidden Markov model (HMM) classifier. The output of the classifier is a prediction of the contact state, which includes no contact, a contact aligned with the central axis of the valve, and an edge contact. To distinguish between contact states, the robot performs exploratory behaviors that produce distinct patterns in the time-series data. The patterns are discovered by first analyzing the data using a probabilistic clustering algorithm that transforms the multidimensional data into a one-dimensional sequence of symbols. The symbols produced by the clustering algorithm are used to train the HMM classifier. We examined two exploratory behaviors: a rotation around the x-axis, and a rotation around the y-axis of the gripper. We show that using these two exploratory behaviors we can successfully predict a contact state with an accuracy of 88 ± 5 % and 81 ± 10 %, respectively.
Date Issued
2014-06
Date Acceptance
2014-05-31
Citation
Proc. IEEE Intl Conf. on Robotics and Automation (ICRA 2014), 2014
Publisher
IEEE
Start Page
6542
End Page
6548
Journal / Book Title
Proc. IEEE Intl Conf. on Robotics and Automation (ICRA 2014)
Copyright Statement
© 2014 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=6907824
Source
ICRA 2014
Publication Status
Published
Start Date
2014-05-31
Finish Date
2014-06-07
Coverage Spatial
Hong Kong