Title
Learning probabilistic discriminative models of grasp affordances under limited supervision
Abstract
This paper addresses the problem of learning and efficiently representing discriminative probabilistic models of object-specific grasp affordances particularly when the number of labeled grasps is extremely limited. The proposed method does not require an explicit 3D model but rather learns an implicit manifold on which it defines a probability distribution over grasp affordances. We obtain hypothetical grasp configurations from visual descriptors that are associated with the contours of an object. While these hypothetical configurations are abundant, labeled configurations are very scarce as these are acquired via time-costly experiments carried out by the robot. Kernel logistic regression (KLR) via joint kernel maps is trained to map the hypothesis space of grasps into continuous class-conditional probability values indicating their achievability. We propose a soft-supervised extension of KLR and a framework to combine the merits of semi-supervised and active learning approaches to tackle the scarcity of labeled grasps. Experimental evaluation shows that combining active and semi-supervised learning is favorable in the existence of an oracle. Furthermore, semi-supervised learning outperforms supervised learning, particularly when the labeled data is very limited.
Year
DOI
Venue
2010
10.1109/IROS.2010.5650088
Intelligent Robots and Systems
Keywords
DocType
ISSN
grippers,learning (artificial intelligence),learning systems,manipulators,probability,regression analysis,3D model,active learning approaches,continuous class conditional probability,grasp affordances,hypothesis space,hypothetical grasp configuration,joint kernel maps,kernel logistic regression,probabilistic discriminative models,probability distribution,semisupervised learning,soft supervised extension,visual descriptors
Conference
2153-0858
ISBN
Citations 
PageRank 
978-1-4244-6674-0
6
0.57
References 
Authors
7
4
Name
Order
Citations
PageRank
Erkan, A.N.160.57
o kromer247238.99
Detry, R.3241.36
yasemin altun42463150.46