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Book Description

This book presents programming by demonstration for robot learning from observations with a focus on the trajectory level of task abstraction

  • Discusses methods for optimization of task reproduction, such as reformulation of task planning as a constrained optimization problem
  • Focuses on regression approaches, such as Gaussian mixture regression, spline regression, and locally weighted regression
  • Concentrates on the use of vision sensors for capturing motions and actions during task demonstration by a human task expert

Table of Contents

  1. Cover
  2. Title Page
  3. Preface
  4. List of Abbreviations
  5. 1 Introduction
    1. 1.1 Robot Programming Methods
    2. 1.2 Programming by Demonstration
    3. 1.3 Historical Overview of Robot PbD
    4. 1.4 PbD System Architecture
    5. 1.5 Applications
    6. 1.6 Research Challenges
    7. 1.7 Summary
    8. References
  6. 2 Task Perception
    1. 2.1 Optical Tracking Systems
    2. 2.2 Vision Cameras
    3. 2.3 Summary
    4. References
  7. 3 Task Representation
    1. 3.1 Level of Abstraction
    2. 3.2 Probabilistic Learning
    3. 3.3 Data Scaling and Aligning
    4. 3.4 Summary
    5. References
  8. 4 Task Modeling
    1. 4.1 Gaussian Mixture Model (GMM)
    2. 4.2 Hidden Markov Model (HMM)
    3. 4.3 Conditional Random Fields (CRFs)
    4. 4.4 Dynamic Motion Primitives (DMPs)
    5. 4.5 Summary
    6. References
  9. 5 Task Planning
    1. 5.1 Gaussian Mixture Regression
    2. 5.2 Spline Regression
    3. 5.3 Locally Weighted Regression
    4. 5.4 Gaussian Process Regression
    5. 5.5 Summary
    6. References
  10. 6 Task Execution
    1. 6.1 Background and Related Work
    2. 6.2 Kinematic Robot Control
    3. 6.3 Vision‐Based Trajectory Tracking Control
    4. 6.4 Image‐Based Task Planning
    5. 6.5 Robust Image‐Based Tracking Control
    6. 6.6 Discussion
    7. 6.7 Summary
    8. References
  11. Index
  12. End User License Agreement