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Robotic tactile perception and under...
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Liu, Huaping.
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Robotic tactile perception and understanding = a sparse coding method /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Robotic tactile perception and understanding/ by Huaping Liu, Fuchun Sun.
其他題名:
a sparse coding method /
作者:
Liu, Huaping.
其他作者:
Sun, Fuchun.
出版者:
Singapore :Springer Singapore : : 2018.,
面頁冊數:
xx, 207 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
標題:
Robot hands. -
電子資源:
http://dx.doi.org/10.1007/978-981-10-6171-4
ISBN:
9789811061714
Robotic tactile perception and understanding = a sparse coding method /
Liu, Huaping.
Robotic tactile perception and understanding
a sparse coding method /[electronic resource] :by Huaping Liu, Fuchun Sun. - Singapore :Springer Singapore :2018. - xx, 207 p. :ill., digital ;24 cm.
This book introduces the challenges of robotic tactile perception and task understanding, and describes an advanced approach based on machine learning and sparse coding techniques. Further, a set of structured sparse coding models is developed to address the issues of dynamic tactile sensing. The book then proves that the proposed framework is effective in solving the problems of multi-finger tactile object recognition, multi-label tactile adjective recognition and multi-category material analysis, which are all challenging practical problems in the fields of robotics and automation. The proposed sparse coding model can be used to tackle the challenging visual-tactile fusion recognition problem, and the book develops a series of efficient optimization algorithms to implement the model. It is suitable as a reference book for graduate students with a basic knowledge of machine learning as well as professional researchers interested in robotic tactile perception and understanding, and machine learning.
ISBN: 9789811061714
Standard No.: 10.1007/978-981-10-6171-4doiSubjects--Topical Terms:
901199
Robot hands.
LC Class. No.: TJ211.43
Dewey Class. No.: 629.8933
Robotic tactile perception and understanding = a sparse coding method /
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This book introduces the challenges of robotic tactile perception and task understanding, and describes an advanced approach based on machine learning and sparse coding techniques. Further, a set of structured sparse coding models is developed to address the issues of dynamic tactile sensing. The book then proves that the proposed framework is effective in solving the problems of multi-finger tactile object recognition, multi-label tactile adjective recognition and multi-category material analysis, which are all challenging practical problems in the fields of robotics and automation. The proposed sparse coding model can be used to tackle the challenging visual-tactile fusion recognition problem, and the book develops a series of efficient optimization algorithms to implement the model. It is suitable as a reference book for graduate students with a basic knowledge of machine learning as well as professional researchers interested in robotic tactile perception and understanding, and machine learning.
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