By Topic

Detecting the functional similarities between tools using a hierarchical representation of outcomes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Jivko Sinapov ; Developmental Robotics Lab, Iowa State University, USA ; Alexadner Stoytchev

The ability to reason about multiple tools and their functional similarities is a prerequisite for intelligent tool use. This paper presents a model which allows a robot to detect the similarity between tools based on the environmental outcomes observed with each tool. To do this, the robot incrementally learns an adaptive hierarchical representation (i.e., a taxonomy) for the types of environmental changes that it can induce and detect with each tool. Using the learned taxonomies, the robot can infer the similarity between different tools based on the types of outcomes they produce. The results show that the robot is able to learn accurate outcome models for six different tools. In addition, the robot was able to detect the similarity between tools using the learned outcome models.

Published in:

2008 7th IEEE International Conference on Development and Learning

Date of Conference:

9-12 Aug. 2008