Learning to Grasp Unknown Objects Based on 3D Edge Information

Leon Bodenhagen, Dirk Kraft, Mila Popovic, Emre Baseski, Peter Eggenberger Hotz, Norbert Krüger

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

In this work we refine an initial grasping behavior based on 3D edge information by learning. Based on a set of autonomously generated evaluated grasps and relations between the semi-global 3D edges, a prediction function is learned that computes a likelihood for the success of a grasp using either an offline or an online learning scheme. Both methods are implemented using a hybrid artificial neural network containing standard nodes with a sigmoid activation function and nodes with a radial basis function. We show that a significant performance improvement can be achieved.
OriginalsprogEngelsk
TitelIEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA2009)
ForlagIEEE
Publikationsdato2010
Sider421 - 428
StatusUdgivet - 2010
BegivenhedThe 8th IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA2009) - DAEJEON, Sydkorea
Varighed: 15. dec. 200918. dec. 2009

Konference

KonferenceThe 8th IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA2009)
Land/OmrådeSydkorea
ByDAEJEON
Periode15/12/200918/12/2009

Fingeraftryk

Dyk ned i forskningsemnerne om 'Learning to Grasp Unknown Objects Based on 3D Edge Information'. Sammen danner de et unikt fingeraftryk.

Citationsformater