Robot programming by demonstration : a probabilistic approach
著者
書誌事項
Robot programming by demonstration : a probabilistic approach
（Engineering sciences, Micro- and nanotechnology）
EPFL Press , CRC Press [distributor], c2009
- EPFL Press
- CRC Press
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注記
Includes bibliographical references and index
内容説明・目次
- 巻冊次
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CRC Press ISBN 9781439808672
内容説明
Also referred to as learning by imitation, tutelage, or apprenticeship learning, Programming by Demonstration (PbD) develops methods by which new skills can be transmitted to a robot. This book examines methods by which robots learn new skills through human guidance. Taking a practical perspective, it covers a broad range of applications, including service robots. The text addresses the challenges involved in investigating methods by which PbD is used to provide robots with a generic and adaptive model of control. Drawing on findings from robot control, human-robot interaction, applied machine learning, artificial intelligence, and developmental and cognitive psychology, the book contains a large set of didactic and illustrative examples. Practical and comprehensive machine learning source codes are available on the book's companion website: http://www.programming-by-demonstration.org
目次
ACKNOWLEDGMENT
INTRODUCTION
Contributions
Organization of the book
Review of Robot Programming by Demonstration (PBD)
Current state of the art in PbD
SYSTEM ARCHITECTURE
Illustration of the proposed probabilistic approach
Encoding of motion in a Gaussian Mixture Model (GMM)
Encoding of motion in Hidden Markov Model (HMM)
Reproduction through Gaussian Mixture Regression (GMR)
Reproduction by considering multiple constraints
Learning of model parameters
Reduction of dimensionality and latent space projection
Model selection and initialization
Regularization of GMM parameters
Use of prior information to speed up the learning process
Extension to mixture models of varying density distributions
Summary of the chapter
COMPARISON AND OPTIMIZATION OF THE PARAMETERS
Optimal reproduction of trajectories through HMM and GMM/GMR
Optimal latent space of motion
Optimal selection of the number of Gaussians
Robustness evaluation of the incremental learning process
HANDLING OF CONSTRAINTS IN JOINT SPACE AND TASK SPACE
Inverse kinematics
Handling of task constraints in joint spaceexperiment with industrial robot
Handling of task constraints in latent spaceexperiment with humanoid robot
EXTENSION TO DYNAMICAL SYSTEM AND HANDLING OF PERTURBATIONS
Proposed dynamical system
Influence of the dynamical system parameters
Experimental setup
Experimental results
TRANSFERRING SKILLS THROUGH ACTIVE TEACHING METHODS
Experimental setup
Experimental results
Roles of an active teaching scenario
USING SOCIAL CUES TO SPEED UP THE LEARNING PROCESS
Experimental setup
Experimental results
DISCUSSION, FUTURE WORK AND CONCLUSIONS
Advantages of the proposed approach
Failures and limitations of the proposed approach
Further issues
Final words
REFERENCES
INDEX
- 巻冊次
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EPFL Press ISBN 9782940222315
目次
System architecture // Comparasion and optimisation of the parameters // Extension to dynamical system and handling of perturbations // Transferring skills through active teaching methods // Using social cues to speed up the learning process // Discussion, Future work and conclusions
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