Paper Info
Title
Robot grasp synthesis from virtual demonstration and topology-preserving environment reconstruction
Abstract
Automatic environment modeling is an essential requirement for intelligent robots to execute manipulation tasks. Object recognition and workspace reconstruction also enable 3D user interaction and programming of assembly operations. In this paper a novel method for synthesizing robot grasps from demonstration is presented. The system allows learning and classification of human grasps demonstrated in virtual reality as well as teaching of robot grasps and simulation of manipulation tasks. Both virtual grasp demonstration and grasp synthesis take advantage of a topology-preserving approach for automatic workspace modeling with a monocular camera. The method is based on the computation of edge-face graphs. The algorithm works in real-time and shows high scalability in the number of objects thus allowing accurate reconstruction and registration from multiple views. Grasp synthesis is performed mimicking the human hand pre-grasp motion with data smoothing. Experiments reported in the paper have tested the capabilities of both the vision algorithm and the grasp synthesizer.
Year
DOI
Venue
2007
10.1109/IROS.2007.4399011
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems
Keywords
Field
DocType
robot grasp synthesis,topology-preserving environment reconstruction,automatic environment modeling,intelligent robots,object recognition,3D user interaction,human grasp learning,virtual reality,monocular camera,edge-face graphs,object registration,human hand pre-grasp motion
Computer vision,GRASP,Virtual reality,Computer science,Workspace,3D user interaction,Artificial intelligence,Robot,Image registration,Cognitive neuroscience of visual object recognition,Scalability
Conference
ISSN
ISBN
Citations 
2153-0858
978-1-4244-0911-2
13
PageRank 
References 
Authors
0.82
17
2
Name
Order
Citations
PageRank
Jacopo Aleotti125929.76
Stefano Caselli231436.32