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A Human Robot Interaction Framework for Robotic Motor Skill Learning

Published: 26 June 2018 Publication History

Abstract

A considerable amount of research in the field of human-robot interaction has shown that a human teacher can be an integral component during the learning process of a robot. In this paper, we propose a learning framework that is based on learning from demonstration at a trajectory level. Specifically, we illustrate a scenario where the Sawyer Robotic Arm must learn to pick and place a specific object according to the demonstration of a human teacher. The purpose of the experiment is to facilitate the effectiveness of the proposed method.

References

[1]
B. Akgun, M. Cakmak, J. W. Yoo, and A. L. Thomaz. Trajectories and keyframes for kinesthetic teaching: A human-robot interaction perspective. Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction, pages 391--398, 2012.
[2]
K. Gräve, J. Stückler, and S. Behnke. Learning motion skills from expert demonstrations and own experience using gaussian process regression. In Robotics (ISR), 2010 41st International Symposium on and 2010 6th German Conference on Robotics (ROBOTIK), pages 1--8. VDE, 2010.
[3]
M. I. Jordan and R. A. Jacobs. Hierarchical mixtures of experts and the em algorithm. Neural computation, 6(2):181--214, 1994.
[4]
M. I. Jordan and D. E. Rumelhart. Forward models: Supervised learning with a distal teacher. Cognitive science, 16(3):307--354, 1992.

Cited By

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  • (2019)Importance of effective teaching in robot motor skill learningProceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3316782.3322775(489-492)Online publication date: 5-Jun-2019

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  1. A Human Robot Interaction Framework for Robotic Motor Skill Learning

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    cover image ACM Other conferences
    PETRA '18: Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference
    June 2018
    591 pages
    ISBN:9781450363907
    DOI:10.1145/3197768
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    • NSF: National Science Foundation

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 26 June 2018

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    Author Tags

    1. Human Robot Interaction
    2. Kinesthetic Teaching
    3. Machine Learning
    4. Neural Networks

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    • (2019)Importance of effective teaching in robot motor skill learningProceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3316782.3322775(489-492)Online publication date: 5-Jun-2019

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