Fast Grasp Learning for Novel Objects

Dawid Seredyński , Wojciech Szynkiewicz

Abstract

This paper presents a method for fast learning of dexterous grasps for unknown objects. We use two probabilistic models of each grasp type learned from a single demonstrated grasp example to generate many grasp candidates for new objects with unknown shapes. These models encode probability density functions representing relationship between fingers and object local features, and whole hand configuration that is particular to a grasp example, respectively. Both, in the training and in the grasp generation stage we use an incomplete 3D point cloud from a depth sensor. The results of simulation experiments performed with the BarrettHand gripper and several objects of different shapes indicate that the proposed learning approach is applicable in realistic scenarios.
Author Dawid Seredyński IAiIS
Dawid Seredyński,,
- The Institute of Control and Computation Engineering
, Wojciech Szynkiewicz IAiIS
Wojciech Szynkiewicz,,
- The Institute of Control and Computation Engineering
Pages681-692
Publication size in sheets0.55
Book Szewczyk Roman, Kaliczyńska Małgorzata, Zieliński Cezary: Challenges in Automation, Robotics and Measurement Techniques. Proceedings of AUTOMATION-2016, March 2-4, 2016, Warsaw, Poland, Advances in Intelligent Systems and Computing, vol. 440, 2016, Springer International Publishing, ISBN 978-3-319-29356-1, [978-3-319-29357-8], 919 p., DOI:10.1007/978-3-319-29357-8
Keywords in EnglishGrasp learning Probabilistic models Kernel density estimation
DOIDOI:10.1007/978-3-319-29357-8_59
URL http://link.springer.com/chapter/10.1007/978-3-319-29357-8_59
projectRobREx: Autonomy for rescue and exploration robots. Project leader: Zieliński Cezary, , Phone: 5102, start date 12-12-2012, end date 30-11-2015, 513/1031, Completed
WEiTI Projects financed by NCRD [Projekty finansowane przez NCBiR (NCBR)]
Languageen angielski
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Sererdynski Szynk automation_2016.pdf (file archived - login or check accessibility on faculty) Sererdynski Szynk automation_2016.pdf 1.24 MB
Score (nominal)15
ScoreMinisterial score = 15.0, 27-03-2017, BookChapterSeriesAndMatConf
Ministerial score (2013-2016) = 15.0, 27-03-2017, BookChapterSeriesAndMatConf
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