Autonomous Active Learning of Task-Relevant Features for Mobile Manipulation

Show full item record

Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/41833

Title: Autonomous Active Learning of Task-Relevant Features for Mobile Manipulation
Author: Nguyen, Hai ; Kemp, Charles C.
Abstract: We present an active learning approach that enables a mobile manipulator to autonomously learn task-relevant features. For a given behavior, our system trains a Support Vector Machine (SVM) that predicts the 3D locations at which the behavior will succeed. This decision is made based on visual features that surround each 3D location. After a quick initialization by the user, the robot efficiently collects and labels positive and negative examples fully autonomously. To demonstrate the efficacy of our approach, we present results for behaviors that flip a light switch up and down, push the top or bottom of a rocker-type light switch, and open or close a drawer. Our implementation uses a Willow Garage PR2 robot. We show that our approach produces classifiers that predict the success of these behaviors. In addition, we show that the robot can continuously learn from its experience. In our initial evaluation of 6 behaviors with learned classifiers, each behavior succeeded in 5 out of 5 trials with at most one retry.
Description: Presented at RSS 2011 Workshop on Mobile Manipulation: Learning to Manipulate, June 27, 2011, University of Southern California, Los Angeles, California, USA.
Type: Proceedings
Post-print
URI: http://hdl.handle.net/1853/41833
Citation: Hai Nguyen, and Charles C. Kemp, “Autonomous Active Learning of Task-Relevant Features for Mobile Manipulation,” RSS 2011 Workshop on Mobile Manipulation: Learning to Manipulate, 2011.
Date: 2011
Contributor: Georgia Institute of Technology. Healthcare Robotics Lab
Georgia Institute of Technology. Center for Robotics and Intelligent Machines
Publisher: Georgia Institute of Technology
Subject: Active learning
Autonomous mobile manipulators
Task-relevant locations in human environments
SVM
Support vector machine

All materials in SMARTech are protected under U.S. Copyright Law and all rights are reserved, unless otherwise specifically indicated on or in the materials.

Files in this item

Files Size Format View
0000057267-Auto ... or Mobile Manipulation.pdf 1.850Mb PDF View/ Open

This item appears in the following Collection(s)

Show full item record