Conceptual Imitation Learning: An Application to Human-robot Interaction

Hossein Hajimirsadeghi, Majid Nili Ahmadabadi, Mostafa Ajallooeian, Babak Araabi, Hadi Moradi
; Proceedings of 2nd Asian Conference on Machine Learning, JMLR Workshop and Conference Proceedings 13:331-346, 2010.

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 paper presents a conceptual approach for imitation learning using feedback cues and interactive training to abstract spatio-temporal demonstrations based on their perceptual and functional characteristics. 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. Performance of the proposed algorithm is evaluated in a human-robot interaction task of imitating signs produced by hand movements. Experimental results show efficiency of our model for concept extraction, symbol emergence, motion pattern recognition, and regeneration.

Cite this Paper


BibTeX
@InProceedings{pmlr-v13-hajimirsadeghi10a, title = {Conceptual Imitation Learning: An Application to Human-robot Interaction}, author = {Hossein Hajimirsadeghi and Majid Nili Ahmadabadi and Mostafa Ajallooeian and Babak Araabi and Hadi Moradi}, booktitle = {Proceedings of 2nd Asian Conference on Machine Learning}, pages = {331--346}, year = {2010}, editor = {Masashi Sugiyama and Qiang Yang}, volume = {13}, series = {Proceedings of Machine Learning Research}, address = {Tokyo, Japan}, month = {08--10 Nov}, publisher = {JMLR Workshop and Conference Proceedings}, pdf = {http://proceedings.mlr.press/v13/hajimirsadeghi10a/hajimirsadeghi10a.pdf}, url = {http://proceedings.mlr.press/v13/hajimirsadeghi10a.html}, 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 paper presents a conceptual approach for imitation learning using feedback cues and interactive training to abstract spatio-temporal demonstrations based on their perceptual and functional characteristics. 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. Performance of the proposed algorithm is evaluated in a human-robot interaction task of imitating signs produced by hand movements. Experimental results show efficiency of our model for concept extraction, symbol emergence, motion pattern recognition, and regeneration.} }
Endnote
%0 Conference Paper %T Conceptual Imitation Learning: An Application to Human-robot Interaction %A Hossein Hajimirsadeghi %A Majid Nili Ahmadabadi %A Mostafa Ajallooeian %A Babak Araabi %A Hadi Moradi %B Proceedings of 2nd Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2010 %E Masashi Sugiyama %E Qiang Yang %F pmlr-v13-hajimirsadeghi10a %I PMLR %J Proceedings of Machine Learning Research %P 331--346 %U http://proceedings.mlr.press %V 13 %W PMLR %X 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 paper presents a conceptual approach for imitation learning using feedback cues and interactive training to abstract spatio-temporal demonstrations based on their perceptual and functional characteristics. 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. Performance of the proposed algorithm is evaluated in a human-robot interaction task of imitating signs produced by hand movements. Experimental results show efficiency of our model for concept extraction, symbol emergence, motion pattern recognition, and regeneration.
RIS
TY - CPAPER TI - Conceptual Imitation Learning: An Application to Human-robot Interaction AU - Hossein Hajimirsadeghi AU - Majid Nili Ahmadabadi AU - Mostafa Ajallooeian AU - Babak Araabi AU - Hadi Moradi BT - Proceedings of 2nd Asian Conference on Machine Learning PY - 2010/10/31 DA - 2010/10/31 ED - Masashi Sugiyama ED - Qiang Yang ID - pmlr-v13-hajimirsadeghi10a PB - PMLR SP - 331 DP - PMLR EP - 346 L1 - http://proceedings.mlr.press/v13/hajimirsadeghi10a/hajimirsadeghi10a.pdf UR - http://proceedings.mlr.press/v13/hajimirsadeghi10a.html AB - 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 paper presents a conceptual approach for imitation learning using feedback cues and interactive training to abstract spatio-temporal demonstrations based on their perceptual and functional characteristics. 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. Performance of the proposed algorithm is evaluated in a human-robot interaction task of imitating signs produced by hand movements. Experimental results show efficiency of our model for concept extraction, symbol emergence, motion pattern recognition, and regeneration. ER -
APA
Hajimirsadeghi, H., Ahmadabadi, M.N., Ajallooeian, M., Araabi, B. & Moradi, H.. (2010). Conceptual Imitation Learning: An Application to Human-robot Interaction. Proceedings of 2nd Asian Conference on Machine Learning, in PMLR 13:331-346

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