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Learning Robot Navigation Behaviors by Demonstration Using a RRT Planner Read chapter Read first chapter

2016 | OriginalPaper | Chapter

A Framework for Modelling Local Human-Robot Interactions Based on Unsupervised Learning

Authors : Rafael Ramón-Vigo, Noé Pérez-Higueras, Fernando Caballero, Luis Merino

Published in: Social Robotics

Publisher: Springer International Publishing

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Abstract

This paper addresses the problem of teaching a robot interaction behaviors using the imitation learning paradigm. Particularly, the approach makes use of Gaussian Mixture Models (GMMs) to model the physical interaction of the robot and the person when the robot is teleoperated or guided by an expert. The learned models are integrated into a sample-based planner, an RRT*, at two levels: as a cost function in order to plan trajectories considering behavior constraints, and as configuration space sampling bias to discard samples with low cost according to the behaviors. The algorithm is successfully tested in the laboratory using an actual robot and real trajectories examples provided by an expert.

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Footnotes
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Metadata
Title
A Framework for Modelling Local Human-Robot Interactions Based on Unsupervised Learning
Authors
Rafael Ramón-Vigo
Noé Pérez-Higueras
Fernando Caballero
Luis Merino
Copyright Year
2016
Book
Social Robotics

Print ISBN: 978-3-319-47436-6

Electronic ISBN: 978-3-319-47437-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-47437-3_4

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