Humanoid Head Face Mechanism with Expandable Facial Expressions

Asheber, W.T.; Lin, Chyi‐Yeu; Yen, Shih Hsiang

doi:10.5772/62181

Cited by 19 publications

(12 citation statements)

References 27 publications

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“…Early works [10]- [16], [28] solely focus on hardware design of the robot face and pre-program the facial expressions. Kismet [20], [29]- [34] generates diverse facial expressions by interpolating among predefined basis facial postures over a three-dimensional space.…”

Section: Related Workmentioning

confidence: 99%

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Smile Like You Mean It: Driving Animatronic Robotic Face with Learned Models

Chen¹,

Hu²,

Li³

et al. 2021

Preprint

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Ability to generate intelligent and generalizable facial expressions is essential for building human-like social robots. At present, progress in this field is hindered by the fact that each facial expression needs to be programmed by humans. In order to adapt robot behavior in real time to different situations that arise when interacting with human subjects, robots need to be able to train themselves without requiring human labels, as well as make fast action decisions and generalize the acquired knowledge to diverse and new contexts. We addressed this challenge by designing a physical animatronic robotic face with soft skin and by developing a vision-based self-supervised learning framework for facial mimicry. Our algorithm does not require any knowledge of the robot's kinematic model, camera calibration or predefined expression set. By decomposing the learning process into a generative model and an inverse model, our framework can be trained using a single motor babbling dataset. Comprehensive evaluations show that our method enables accurate and diverse face mimicry across diverse human subjects.

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Section: Related Workmentioning

confidence: 99%

“…The key limitation is the lack of a general learning framework that can learn from limited human supervision. Some traditional methods [10]- [16] define a set of pre-specified facial expressions. Others generalize this process to search for closest match from a database [17] or by following an fitness function [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Smile Like You Mean It: Driving Animatronic Robotic Face with Learned Models

Chen¹,

Hu²,

Li³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…However, although numerous studies have developed androids for emotional interactions (Kobayashi and Hara, 1993;Kobayashi et al, 2000;Minato et al, 2004Minato et al, , 2006Minato et al, , 2007Weiguo et al, 2004;Ishihara et al, 2005;Matsui et al, 2005;Berns and Hirth, 2006;Blow et al, 2006;Hashimoto et al, 2006Hashimoto et al, , 2008Oh et al, 2006;Sakamoto et al, 2007;Lee et al, 2008;Takeno et al, 2008;Allison et al, 2009;Lin et al, 2009Lin et al, , 2016Kaneko et al, 2010;Becker-Asano and Ishiguro, 2011;Ahn et al, 2012;Mazzei et al, 2012;Tadesse and Priya, 2012;Cheng et al, 2013;Habib et al, 2014;Yu et al, 2014;Asheber et al, 2016;Glas et al, 2016;Marcos et al, 2016;Faraj et al, 2021;Nakata et al, 2021; Table 1), few have empirically validated the androids that were developed. First, no study validated androids' AUs coded using FACS (Ekman and Friesen, 1978;Ekman et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

An Android for Emotional Interaction: Spatiotemporal Validation of Its Facial Expressions

et al. 2022

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Android robots capable of emotional interactions with humans have considerable potential for application to research. While several studies developed androids that can exhibit human-like emotional facial expressions, few have empirically validated androids’ facial expressions. To investigate this issue, we developed an android head called Nikola based on human psychology and conducted three studies to test the validity of its facial expressions. In Study 1, Nikola produced single facial actions, which were evaluated in accordance with the Facial Action Coding System. The results showed that 17 action units were appropriately produced. In Study 2, Nikola produced the prototypical facial expressions for six basic emotions (anger, disgust, fear, happiness, sadness, and surprise), and naïve participants labeled photographs of the expressions. The recognition accuracy of all emotions was higher than chance level. In Study 3, Nikola produced dynamic facial expressions for six basic emotions at four different speeds, and naïve participants evaluated the naturalness of the speed of each expression. The effect of speed differed across emotions, as in previous studies of human expressions. These data validate the spatial and temporal patterns of Nikola’s emotional facial expressions, and suggest that it may be useful for future psychological studies and real-life applications.

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“…Prince and Suliman [1] proposed the Artificial Brain Emotion Recognition and Generation System (ABERGS) and described a method for recognizing the emotion from the human being and generating as a robotic mood state using Fuzzy Kohonen Clustering Network (FKCN) logic. Wagshum et al, [2] reported a new mechanism design for the humanoid robot head with the basic facial expressions. The approach is a cost effective one and with lesser complexities.…”

Section: Introductionmentioning

confidence: 99%

An Automated Research for Emotion Recognition and Generation

2019

ijrte

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As of today, Human Robot Interaction (HRI) is considered as an on-going and challenging trend. The development in this field is vast and still in budding phase with respect to research. Several Algorithms and Models are being designed that allow the robots to precept think and behave like a human being. Scientists are working regressively on creating an ‘Artificial Mind’ as an infrastructure using computational methods which are specifically delineated to guide a robot. Artificial Intelligence has taken the next level which is called as ‘Embodied Artificial Intelligence’ in order to make the humanoids smarter. This research deals with, a practical robot head which recognizes and mirrors the same basic emotions from the user with the presence of visual inputs and Facial Action Code System (FACS) control points on the human face. This research work deals with the design and development of three major modules in which, one module involves the fabrication of the practical robot head to represent the emotions. The next major module is the Artificial Intelligence segment including a Fuzzy Logic. In this segment, the inputs will be taken from the user in the form intensities of visual signals. Based on the Fuzzy Logic classifications; the respective emotion from the user is recognized and fed forward to the practical head. The third module of this research deals with the interfacing mechanism between the Fuzzy Logic module and the Mechanical Humanoid head for traversing the signals generated from the Fuzzy Logic Chunk to the practical robot head in the form mechanical actions. This proposed research model will have a huge impact in the study of interaction between the Human and Robots and research studies on certain critical diseases or syndromes like ASD, Alzheimer’s disease, and Bell’s palsy

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Humanoid Head Face Mechanism with Expandable Facial Expressions

Cited by 19 publications

References 27 publications

Smile Like You Mean It: Driving Animatronic Robotic Face with Learned Models

Smile Like You Mean It: Driving Animatronic Robotic Face with Learned Models

An Android for Emotional Interaction: Spatiotemporal Validation of Its Facial Expressions

An Automated Research for Emotion Recognition and Generation

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