Paper Info
Title
Autonomous active recognition and unfolding of clothes using random decision forests and probabilistic planning
Abstract
We present a novel approach to the problem of autonomously recognizing and unfolding articles of clothing using a dual manipulator. The problem consists of grasping an article from a random point, recognizing it and then bringing it into an unfolded state. We propose a data-driven method for clothes recognition from depth images using Random Decision Forests. We also propose a method for unfolding an article of clothing after estimating and grasping two key-points, using Hough forests. Both methods are implemented into a POMDP framework allowing the robot to interact optimally with the garments, taking into account uncertainty in the recognition and point estimation process. This active recognition and unfolding makes our system very robust to noisy observations. Our methods were tested on regular-sized clothes using a dual-arm manipulator and an Xtion depth sensor. We achieved 100% accuracy in active recognition and 93.3% unfolding success rate, while our system operates faster compared to the state of the art.
Year
DOI
Venue
2014
10.1109/ICRA.2014.6906974
Robotics and Automation
Keywords
Field
DocType
clothing,decision trees,dexterous manipulators,grippers,image sensors,object recognition,probability,robot vision,Hough forests,POMDP framework,Xtion depth sensor,autonomous active recognition,clothes recognition,clothes unfolding,dual arm manipulator,grasping,point estimation process,probabilistic planning,random decision forests
Point estimation,Computer vision,Partially observable Markov decision process,Clothing,Feature extraction,Artificial intelligence,Probabilistic logic,Engineering,Robot,Random forest,Perception
Conference
Volume
Issue
ISSN
2014
1
1050-4729
Citations 
PageRank 
References 
12
0.60
15
Authors
4
Name
Order
Citations
PageRank
Andreas Doumanoglou1644.38
Andreas Kargakos2393.63
Tae-Kyun Kim31987129.30
S. Malassiotis443626.62