research-article

Conceptual Imitation Learning in a Human-Robot Interaction Paradigm

Authors:

Hossein Hajimirsadeghi,

Majid Nili Ahmadabadi,

Babak Nadjar Araabi,

Hadi MoradiAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 3, Issue 2

Article No.: 28, Pages 1 - 25

https://doi.org/10.1145/2089094.2089104

Published: 01 February 2012 Publication History

Abstract

In general, imitation is imprecisely used to address different levels of social learning from high-level knowledge transfer to low-level regeneration of motor commands. However, true imitation is based on abstraction and conceptualization. This article presents a model for conceptual imitation through interaction with the teacher to abstract spatio-temporal demonstrations based on their functional meaning. Abstraction, concept acquisition, and self-organization of proto-symbols are performed through an incremental and gradual learning algorithm. In this algorithm, Hidden Markov Models (HMMs) are used to abstract perceptually similar demonstrations. However, abstract (relational) concepts emerge as a collection of HMMs irregularly scattered in the perceptual space but showing the same functionality. Performance of the proposed algorithm is evaluated in two experimental scenarios. The first one is a human-robot interaction task of imitating signs produced by hand movements. The second one is a simulated interactive task of imitating whole body motion patterns of a humanoid model. Experimental results show efficiency of our model for concept extraction, proto-symbol emergence, motion pattern recognition, prediction, and generation.

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Cited By

Xie HJia GAl-Khulaqui MGao ZGuo XFukuda TShi Q(2022)A Motion Generation Strategy of Robotic Rat Using Imitation Learning for Behavioral InteractionIEEE Robotics and Automation Letters10.1109/LRA.2022.31824727:3(7351-7358)Online publication date: Jul-2022
https://doi.org/10.1109/LRA.2022.3182472
Alibeigi MAhmadabadi MAraabi B(2018)A Fast, Robust, and Incremental Model for Learning High-Level Concepts From Human Motions by ImitationIEEE Transactions on Robotics10.1109/TRO.2016.262381733:1(153-168)Online publication date: 20-Dec-2018
https://dl.acm.org/doi/10.1109/TRO.2016.2623817
Hajimirsadeghi HAhmadabadi MAraabi B(2018)Conceptual Imitation Learning Based on Perceptual and Functional Characteristics of ActionIEEE Transactions on Autonomous Mental Development10.1109/TAMD.2013.22638335:4(311-325)Online publication date: 12-Dec-2018
https://dl.acm.org/doi/10.1109/TAMD.2013.2263833
Show More Cited By

Index Terms

Conceptual Imitation Learning in a Human-Robot Interaction Paradigm
1. Applied computing
  1. Operations research
    1. Industry and manufacturing
      1. Command and control
2. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 3, Issue 2

February 2012

455 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2089094

Issue’s Table of Contents

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 February 2012

Accepted: 01 April 2011

Revised: 01 April 2011

Received: 01 December 2010

Published in TIST Volume 3, Issue 2

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Cited By

Xie HJia GAl-Khulaqui MGao ZGuo XFukuda TShi Q(2022)A Motion Generation Strategy of Robotic Rat Using Imitation Learning for Behavioral InteractionIEEE Robotics and Automation Letters10.1109/LRA.2022.31824727:3(7351-7358)Online publication date: Jul-2022
https://doi.org/10.1109/LRA.2022.3182472
Alibeigi MAhmadabadi MAraabi B(2018)A Fast, Robust, and Incremental Model for Learning High-Level Concepts From Human Motions by ImitationIEEE Transactions on Robotics10.1109/TRO.2016.262381733:1(153-168)Online publication date: 20-Dec-2018
https://dl.acm.org/doi/10.1109/TRO.2016.2623817
Hajimirsadeghi HAhmadabadi MAraabi B(2018)Conceptual Imitation Learning Based on Perceptual and Functional Characteristics of ActionIEEE Transactions on Autonomous Mental Development10.1109/TAMD.2013.22638335:4(311-325)Online publication date: 12-Dec-2018
https://dl.acm.org/doi/10.1109/TAMD.2013.2263833
Fonooni BHellström TJanlert L(2015)Priming as a Means to Reduce Ambiguity in Learning from DemonstrationInternational Journal of Social Robotics10.1007/s12369-015-0292-08:1(5-19)Online publication date: 18-Mar-2015
https://doi.org/10.1007/s12369-015-0292-0
Mahmoodian MMoradi HAhmadabadi MAraabi B(2013)Hierarchical concept learning based on functional similarity of actions2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)10.1109/ICRoM.2013.6510072(1-6)Online publication date: Feb-2013
https://doi.org/10.1109/ICRoM.2013.6510072

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