Paper Info
Title
Slip Detection on Natural Objects with a Biomimetic Tactile Sensor.
Abstract
Slip detection enables robotic hands to perform complex manipulation tasks by predicting when a held object is about to be dropped. Here we use a support vector machine classifier to detect slip with a biomimetic optical tactile sensor: the TacTip. Previously, this method has been shown to be effective on various artificial stimuli such as flat or curved surfaces. Here, we investigate whether this method generalises to novel, everyday objects. Five different objects are tested which vary in shape, weight, compliance and texture as well as being common objects that one might encounter day-to-day. Success of up to 90% is achieved which demonstrates the classifier's ability to generalise to a variety of previously unseen, natural objects.
Year
DOI
Venue
2018
10.1007/978-3-319-95972-6_24
BIOMIMETIC AND BIOHYBRID SYSTEMS
Keywords
Field
DocType
Slip detection,Tactile sensing,Machine learning
Computer vision,Robotic hand,Support vector machine classifier,Computer science,Slip (materials science),Artificial intelligence,Classifier (linguistics),Tactile sensor
Conference
Volume
ISSN
Citations 
10928
0302-9743
0
PageRank 
References 
Authors
0.34
4
2
Name
Order
Citations
PageRank
Jasper W. James191.63
Nathan F. Lepora2121.79