Title
Learning a dictionary of prototypical grasp-predicting parts from grasping experience
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.
Year
DOI
Venue
2013
10.1109/ICRA.2013.6630635
Robotics and Automation
Keywords
Field
DocType
grippers,robots,grasping experience,grasping strategy,object grasping,prototypical grasp-predicting parts,real-world grasp,real-world robotic agent,similarity measure
GRASP,Similarity measure,Artificial intelligence,Engineering,Robot,Grippers,Robotics
Conference
ISSN
ISBN
Citations 
1050-4729
978-1-4673-5641-1
12
PageRank 
References 
Authors
0.79
0
4
Name
Order
Citations
PageRank
Renaud Detry118313.94
carl henrik ek232730.76
Marianna Madry3583.15
Danica Kragic42070142.17