Designing of android head system by applying facial muscle mechanism of humans

Ahn, Ho Seok; Lee, Dong‐Wook; Choi, Dongwoon; Lee, Duk-Yeon; Hur, Manhong; Lee, Ho-Gil

doi:10.1109/humanoids.2012.6651611

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…Various methods have been proposed for generating several types of facial expressions in android robots [5][6][7][8][9][10][11]. However, most of these methods are based on FACS (facial action coding system [26]) for positioning and controlling the actuators to reproduce humanlike facial expressions or for modeling skin deformation based on mechanical deformation models.…”

Section: Related Workmentioning

confidence: 99%

“…Since android robots have a highly humanlike appearance, natural communication with humans can be achieved through several types of nonverbal information, such as facial expressions and head/body gestures. There are numerous studies regarding facial expression generation in robots [3][4][5][6][7][8][9][10][11]. Most of these are related to symbolic (static) facial expression of the six traditional emotions (happy, sad, anger, disgust, fear, and surprise).…”

Section: Introductionmentioning

confidence: 99%

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Motion Generation during Vocalized Emotional Expressions and Evaluation in Android Robots

Ishi¹

2020

Becoming Human With Humanoid - From Physical Interaction to Social Intelligence

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Vocalized emotional expressions such as laughter and surprise often occur in natural dialogue interactions and are important factors to be considered in order to achieve smooth robot-mediated communication. Miscommunication may be caused if there is a mismatch between audio and visual modalities, especially in android robots, which have a highly humanlike appearance. In this chapter, motion generation methods are introduced for laughter and vocalized surprise events, based on analysis results of human behaviors during dialogue interactions. The effectiveness of controlling different modalities of the face, head, and upper body (eyebrow raising, eyelid widening/narrowing, lip corner/cheek raising, eye blinking, head motion, and torso motion control) and different motion control levels are evaluated using an android robot. Subjective experiments indicate the importance of each modality in the perception of motion naturalness (humanlikeness) and the degree of emotional expression.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Motion Generation during Vocalized Emotional Expressions and Evaluation in Android Robots

Ishi¹

2020

Becoming Human With Humanoid - From Physical Interaction to Social Intelligence

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“…Face animatronics require complex hardware and software design. Although past work has resulted in impressive humanlike face robots, these rely mostly on preprogrammed facial animations (31)(32)(33)(34)(35)(36)(37). These expressions are usually carefully preprogrammed, tuned, and choreographed rather than being spontaneous.…”

Section: Introductionmentioning

confidence: 99%

Human-robot facial coexpression

Hu,

Chen,

Lin

et al. 2024

Sci. Robot.

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Large language models are enabling rapid progress in robotic verbal communication, but nonverbal communication is not keeping pace. Physical humanoid robots struggle to express and communicate using facial movement, relying primarily on voice. The challenge is twofold: First, the actuation of an expressively versatile robotic face is mechanically challenging. A second challenge is knowing what expression to generate so that the robot appears natural, timely, and genuine. Here, we propose that both barriers can be alleviated by training a robot to anticipate future facial expressions and execute them simultaneously with a human. Whereas delayed facial mimicry looks disingenuous, facial coexpression feels more genuine because it requires correct inference of the human’s emotional state for timely execution. We found that a robot can learn to predict a forthcoming smile about 839 milliseconds before the human smiles and, using a learned inverse kinematic facial self-model, coexpress the smile simultaneously with the human. We demonstrated this ability using a robot face comprising 26 degrees of freedom. We believe that the ability to coexpress simultaneous facial expressions could improve human-robot interaction.

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