Object Recognition Using Tactile Measurements: Kernel Sparse Coding Methods

Dexterous robots have emerged in the last decade in response to the need for fine-motor-control assistance in applications as diverse as surgery, undersea welding, and mechanical manipulation in space. Crucial to the fine operation and contact environmental perception are tactile sensors that are fi...

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Vydáno v:IEEE transactions on instrumentation and measurement Ročník 65; číslo 3; s. 656 - 665
Hlavní autoři: Liu, Huaping, Guo, Di, Sun, Fuchun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.03.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Classification
Coding
Contact
Dexterous robot
Encoding
joint sparse coding
Kernel
kernel sparse coding
Kernels
Object recognition
Robots
Sensors
Silicon
Surface layer
Tactile
tactile measurement
Texture
Training
object recognition
kernel sparse coding
Dexterous robot
tactile measurement
joint sparse coding
ISSN:0018-9456, 1557-9662
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Shrnutí:Dexterous robots have emerged in the last decade in response to the need for fine-motor-control assistance in applications as diverse as surgery, undersea welding, and mechanical manipulation in space. Crucial to the fine operation and contact environmental perception are tactile sensors that are fixed on the robotic fingertips. These can be used to distinguish material texture, roughness, spatial features, compliance, and friction. In this paper, we regard the investigated tactile data as time sequences, of which dissimilarity can be evaluated by the popular dynamic time warping method. A kernel sparse coding method is therefore developed to address the tactile data representation and classification problem. However, the naive use of sparse coding neglects the intrinsic relation between individual fingers, which simultaneously contact the object. To tackle this problem, we develop a joint kernel sparse coding model to solve the multifinger tactile sequence classification problem. In this model, the intrinsic relations between fingers are explicitly taken into account using the joint sparse coding, which encourages all of the coding vectors to share the same sparsity support pattern. The experimental results show that the joint sparse coding achieves better performance than conventional sparse coding.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2016.2514779