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© 2018

Robotic Tactile Perception and Understanding

A Sparse Coding Method

Benefits

  • Provides a systematic and comprehensive introduction to robotic tactile perception and understanding

  • Introduces machine-learning-based solutions for tactile perception and understanding

  • Showcase the applications of sparse coding methods

Book

Table of contents

  1. Front Matter
    Pages i-xx
  2. Background

    1. Front Matter
      Pages 1-1
    2. Huaping Liu, Fuchun Sun
      Pages 3-32
    3. Huaping Liu, Fuchun Sun
      Pages 33-44
  3. Tactile Perception

  4. Visual–Tactile Fusion Perception

    1. Front Matter
      Pages 133-133
  5. Conclusions

    1. Front Matter
      Pages 203-203
    2. Huaping Liu, Fuchun Sun
      Pages 205-207

About this book

Introduction

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.

Keywords

Robots Perception Sparse Coding Tactile Perception Visual Perception Machine Learning Visual-tactile Fusion Object Recognition Dictionary Learning Tactile Adjective Understanding Tactile Material Identification

Authors and affiliations

  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

About the authors

Huaping Liu is an associate professor at the Department of Computer Science and Technology, Tsinghua University. He serves as an associate editor for various journals, including IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Industrial Informatics, IEEE Robotics & Automation Letters, Neurocomputing, Cognitive Computation. He has served as an associate editor for ICRA and IROS and on IJCAI, RSS, and IJCNN Program Committees. His research interests include robotic perception and learning.

Fuchun Sun is a full professor at the Department of Computer Science and Technology, Tsinghua University. He is the recipient of National Science Fund for Distinguished Young Scholars. He serves as an associate editor for a number of international journals, including IEEE Transactions on Systems, Man and Cybernetics: Systems, IEEE Transactions on Fuzzy Systems, Mechatronics, Robotics and Autonomous Systems. His research interests include intelligent control and robotics.

Bibliographic information

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