Learning Control

Sylvain Calinon; Dongheui Lee

doi:10.1007/978-94-007-6046-2_68

Learning Control

Sylvain Calinon, Dongheui Lee

Computation, Information and Technology

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

45 Scopus citations

Abstract

This chapter presents an overview of learning approaches for the acquisition of controllers and movement skills in humanoid robots. The term learning control refers to the process of acquiring a control strategy to achieve a task. While the definition is in some cases restrained to trial-and-error learning, we present here learning control in a broader perspective, with a focus on the representation of skills to be acquired, and on the different learning strategies that can contribute to the acquisition of robust and adaptive controllers for humanoids.

Original language	English
Title of host publication	Humanoid Robotics
Subtitle of host publication	A Reference
Publisher	Springer Netherlands
Pages	1261-1312
Number of pages	52
ISBN (Electronic)	9789400760462
ISBN (Print)	9789400760455
DOIs	https://doi.org/10.1007/978-94-007-6046-2_68
State	Published - 1 Jan 2018

Keywords

Dynamic Movement Primitives (DMP)
Gaussian Mixture Regression (GMR)
Hidden semi-Markov Model (HSMM)
Humanoid Robotics
Kinesthetic Teaching

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10.1007/978-94-007-6046-2_68

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Learning Control

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Keywords

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