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