Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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A Study on Human-Robot Interface based on Imitative Learning using Computational Model of Mirror Neuron System

Mirror Neuron System 계산 모델을 이용한 모방학습 기반 인간-로봇 인터페이스에 관한 연구

  • Ko, Kwang-Enu (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)
  • 고광은 (중앙대학교 전자전기공학부) ;
  • 심귀보 (중앙대학교 전자전기공학부)
  • Received : 2013.09.01
  • Accepted : 2013.11.12
  • Published : 2013.12.25
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Abstract

The mirror neuron regions which are distributed in cortical area handled a functionality of intention recognition on the basis of imitative learning of an observed action which is acquired from visual-information of a goal-directed action. In this paper an automated intention recognition system is proposed by applying computational model of mirror neuron system to the human-robot interaction system. The computational model of mirror neuron system is designed by using dynamic neural networks which have model input which includes sequential feature vector set from the behaviors from the target object and actor and produce results as a form of motor data which can be used to perform the corresponding intentional action through the imitative learning and estimation procedures of the proposed computational model. The intention recognition framework is designed by a system which has a model input from KINECT sensor and has a model output by calculating the corresponding motor data within a virtual robot simulation environment on the basis of intention-related scenario with the limited experimental space and specified target object.

영장류 대뇌 피질 영역 중 거울 뉴런들이 분포한 것으로 추정되는 몇몇 영역은 목적성 행위에 대한 시각 정보를 기반으로 모방학습을 수행함으로써 관측 행동의 의도 인식 기능을 담당한다고 알려졌다. 본 논문은 이러한 거울 뉴런 영역을 모델링 하여 인간-로봇 상호작용 시스템에 적용함으로써, 자동화 된 의도인식 시스템을 개발하고자 한다. 거울 뉴런 시스템 계산 모델은 동적 신경망을 기반으로 구축하였으며, 모델의 입력은 객체와 행위자 동작에 대한 연속된 특징 벡터 집합이고 모델의 모방학습 및 추론과정을 통해 관측자가 수행할 수 있는 움직임 정보를 출력한다. 이를 위해 제한된 실험 공간 내에서 특정 객체와 그에 대한 행위자의 목적성 행동, 즉 의도에 대한 시나리오를 전제로 키넥트 센서를 통해 모델 입력 데이터를 수집하고 가상 로봇 시뮬레이션 환경에서 대응하는 움직임 정보를 계산하여 동작을 수행하는 프레임워크를 개발하였다.

Keywords

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