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Full Description
Imitation underlies our culture, language, and feelings, and robotic imitation has social aspects that are usually ignored. However, roboticists are now starting to understand the power of imitation learning, utilizing animal and human imitation as inspiration. This book provides a reference to state-of-the-art techniques in imitation, demonstration learning, and apprenticeship learning. Recent case studies are given to help the reader appreciate the subtleties of each outlined technique and provide a starting point for new ideas and algorithms. The book is supported by a MATLAB toolbox that provides full implementation of most of the algorithms discussed for a hands-on learning approach.
Contents
Introduction. ImitationAnimals and Humans. Challenges of Imitation in Robotics. Tackling the Correspondence Problem. Action Segmentation and Significance Estimation. Evaluation and Fluid Imitation. Modeling Demonstrations. Inverse Optimal Control. Inverse Reinforcement Learning. Dynamic Motor Primitives. HMM Approaches. Gaussian Mixture Approaches. Gaussian Processes for LfD. Symbolization and Other Approaches.
