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Learning grasping points with shape context
KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.ORCID-id: 0000-0003-2965-2953
2010 (engelsk)Inngår i: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 58, nr 4, s. 362-377Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper presents work on vision based robotic grasping. The proposed method adopts a learning framework where prototypical grasping points are learnt from several examples and then used on novel objects. For representation purposes, we apply the concept of shape context and for learning we use a supervised learning approach in which the classifier is trained with labelled synthetic images. We evaluate and compare the performance of linear and non-linear classifiers. Our results show that a combination of a descriptor based on shape context with a non-linear classification algorithm leads to a stable detection of grasping points for a variety of objects.

sted, utgiver, år, opplag, sider
2010. Vol. 58, nr 4, s. 362-377
Emneord [en]
Grasping, Shape context, Affordances, SVM
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-28268DOI: 10.1016/j.robot.2009.10.003ISI: 000276666100003Scopus ID: 2-s2.0-77249167134OAI: oai:DiVA.org:kth-28268DiVA, id: diva2:387474
Forskningsfinansiär
EU, FP7, Seventh Framework Programme, IST-FP7-IP GRASP (2008-2012)
Merknad
QC 20110114Tilgjengelig fra: 2011-01-14 Laget: 2011-01-12 Sist oppdatert: 2020-03-10bibliografisk kontrollert

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