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Bridging the Robot Perception Gap with Mid-Level Vision Read chapter Read first chapter

2018 | OriginalPaper | Chapter

A Probabilistic Framework for Semi-autonomous Robots Based on Interaction Primitives with Phase Estimation

Authors : Guilherme Maeda, Gerhard Neumann, Marco Ewerton, Rudolf Lioutikov, Jan Peters

Published in: Robotics Research

Publisher: Springer International Publishing

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Abstract

This paper proposes an interaction learning method suited for semi-autonomous robots that work with or assist a human partner. The method aims at generating a collaborative trajectory of the robot as a function of the current action of the human. The trajectory generation is based on action recognition and prediction of the human movement given intermittent observations of his/her positions under unknown speeds of execution; a problem typically found when using motion capture systems in occluded scenarios. Of particular interest, the ability to predict the human movement while observing the initial part of the trajectory, allows for faster robot reactions. The method is based on probabilistically modelling the coupling between human-robot movement primitives and eliminates the need of time-alignment of the training data while being scalable in relation to the number of tasks. We evaluated the method using a 7-DoF lightweight robot arm equipped with a 5-finger hand in a multi-task collaborative assembly experiment, also comparing results with our previous method based on time-aligned trajectories.

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Footnotes
1
The interested reader is referred to our previous works for additional and detailed literature review in respect to their corresponding contributions.
 
2
Although not used in this paper, the ProMP framework also provides means to compute the feedback controller and the interested reader is referred to [15].
 
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Metadata
Title
A Probabilistic Framework for Semi-autonomous Robots Based on Interaction Primitives with Phase Estimation
Authors
Guilherme Maeda
Gerhard Neumann
Marco Ewerton
Rudolf Lioutikov
Jan Peters
Copyright Year
2018
Book
Robotics Research

Print ISBN: 978-3-319-60915-7

Electronic ISBN: 978-3-319-60916-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-60916-4_15