Developmental Approach for Behavior Learning Using Primitive Motion Skills

Dawood, Farhan and Loo, Chu Kiong (2018) Developmental Approach for Behavior Learning Using Primitive Motion Skills. International Journal of Neural Systems, 28 (04). p. 1750038. ISSN 0129-0657

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Official URL: https://doi.org/10.1142/S0129065717500381

Abstract

Imitation learning through self-exploration is essential in developing sensorimotor skills. Most developmental theories emphasize that social interactions, especially understanding of observed actions, could be first achieved through imitation, yet the discussion on the origin of primitive imitative abilities is often neglected, referring instead to the possibility of its innateness. This paper presents a developmental model of imitation learning based on the hypothesis that humanoid robot acquires imitative abilities as induced by sensorimotor associative learning through self-exploration. In designing such learning system, several key issues will be addressed: Automatic segmentation of the observed actions into motion primitives using raw images acquired from the camera without requiring any kinematic model; incremental learning of spatiooral motion sequences to dynamically generates a topological structure in a self-stabilizing manner; organization of the learned data for easy and efficient retrieval using a dynamic associative memory; and utilizing segmented motion primitives to generate complex behavior by the combining these motion primitives. In our experiment, the self-posture is acquired through observing the image of its own body posture while performing the action in front of a mirror through body babbling. The complete architecture was evaluated by simulation and real robot experiments performed on DARwIn-OP humanoid robot.

Item Type: Article
Uncontrolled Keywords: Robot behavior learning; adaptive resonance theory; gaussian distribution; hidden Markov model; incremental learning; topological map; motion primitives
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Divisions: Faculty of Computer Science & Information Technology > Dept of Artificial Intelligence
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 30 Aug 2019 04:04
Last Modified: 30 Aug 2019 04:04
URI: http://eprints.um.edu.my/id/eprint/22153

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