A random forest application to contact-state classification for robot programming by human demonstration Articles uri icon

publication date

  • April 2016

start page

  • 209

end page

  • 227

issue

  • 2

volume

  • 32

international standard serial number (ISSN)

  • 1524-1904

electronic international standard serial number (EISSN)

  • 1526-4025

abstract

  • This paper addresses the non-parametric estimation of the stochastic process related to the classification problem that arises in robot programming by demonstration of compliant motion tasks. Robot programming by demonstration is a robot programming paradigm in which a human operator demonstrates the task to be performed by the robot. In such demonstration, several observable variables, such as velocities and forces can be modeled, non-parametrically, in order to classify the current state of a contact between an object manipulated by the robot and the environment in which it operates. Essential actions in compliant motion tasks are the contacts that take place, and therefore, it is important to understand the sequence of contact states made during a demonstration, called contact classification. We propose a contact classification algorithm based on the random forest algorithm. The main advantage of this approach is that it does not depend on the geometric model of the objects involved in the demonstration. Moreover, it does not rely on the kinestatic model of the contact interactions. The comparison with state-of-the-art contact classifiers shows that random forest classifier is more accurate. Copyright (c) 2015 John Wiley & Sons, Ltd.

keywords

  • multi-class contact classification; sensor force analysis; supervised learning; compliant-motion tasks; specification; uncertainty