• 제목/요약/키워드: Lifelike Facial Expression

검색결과 2건 처리시간 0.015초

감정 경계를 이용한 로봇의 생동감 있는 얼굴 표정 구현 (Life-like Facial Expression of Mascot-Type Robot Based on Emotional Boundaries)

  • 박정우;김우현;이원형;정명진
    • 로봇학회논문지
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    • 제4권4호
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    • pp.281-288
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    • 2009
  • Nowadays, many robots have evolved to imitate human social skills such that sociable interaction with humans is possible. Socially interactive robots require abilities different from that of conventional robots. For instance, human-robot interactions are accompanied by emotion similar to human-human interactions. Robot emotional expression is thus very important for humans. This is particularly true for facial expressions, which play an important role in communication amongst other non-verbal forms. In this paper, we introduce a method of creating lifelike facial expressions in robots using variation of affect values which consist of the robot's emotions based on emotional boundaries. The proposed method was examined by experiments of two facial robot simulators.

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얼굴로봇 Buddy의 기능 및 구동 메커니즘 (Functions and Driving Mechanisms for Face Robot Buddy)

  • 오경균;장명수;김승종;박신석
    • 로봇학회논문지
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    • 제3권4호
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    • pp.270-277
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    • 2008
  • The development of a face robot basically targets very natural human-robot interaction (HRI), especially emotional interaction. So does a face robot introduced in this paper, named Buddy. Since Buddy was developed for a mobile service robot, it doesn't have a living-being like face such as human's or animal's, but a typically robot-like face with hard skin, which maybe suitable for mass production. Besides, its structure and mechanism should be simple and its production cost also should be low enough. This paper introduces the mechanisms and functions of mobile face robot named Buddy which can take on natural and precise facial expressions and make dynamic gestures driven by one laptop PC. Buddy also can perform lip-sync, eye-contact, face-tracking for lifelike interaction. By adopting a customized emotional reaction decision model, Buddy can create own personality, emotion and motive using various sensor data input. Based on this model, Buddy can interact probably with users and perform real-time learning using personality factors. The interaction performance of Buddy is successfully demonstrated by experiments and simulations.

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