Learning a dictionary of prototypical grasp-predicting parts from grasping experience
- 1 May 2013
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
We present a real-world robotic agent that is capable of transferring grasping strategies across objects that share similar parts. The agent transfers grasps across objects by identifying, from examples provided by a teacher, parts by which objects are often grasped in a similar fashion. It then uses these parts to identify grasping points onto novel objects. We focus our report on the definition of a similarity measure that reflects whether the shapes of two parts resemble each other, and whether their associated grasps are applied near one another. We present an experiment in which our agent extracts five prototypical parts from thirty-two real-world grasp examples, and we demonstrate the applicability of the prototypical parts for grasping novel objects.Keywords
This publication has 29 references indexed in Scilit:
- Generalizing grasps across partly similar objectsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Learning Grasp Affordance DensitiesPaladyn, Journal of Behavioral Robotics, 2011
- Robotic Grasping of Novel Objects using VisionThe International Journal of Robotics Research, 2008
- Shape from symmetryPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Efficient matching of pictorial structuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Normalized cuts and image segmentationIeee Transactions On Pattern Analysis and Machine Intelligence, 2000
- Kernel principal component analysisLecture Notes in Computer Science, 1997
- Robot Grasp Synthesis Algorithms: A SurveyThe International Journal of Robotics Research, 1996
- Estimating the Dimension of a ModelThe Annals of Statistics, 1978
- The Representation and Matching of Pictorial StructuresIEEE Transactions on Computers, 1973