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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/16930

Title: Conditional Generative Adversarial Network for Generating Communicative Robot Gestures
Authors: Tuyen, Nguyen Tan Viet
Elibol, Armagan
Chong, Nak Young
Issue Date: 2020-08-31
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)
Start page: 201
End page: 207
DOI: 10.1109/RO-MAN47096.2020.9223498
Abstract: Non-verbal behaviors have an indispensable role for social robots, which help them to interact with humans in a facile and transparent way. Especially, communicative gestures allow robots to have the capability of using bodily expressions for emphasizing the meaning of their speech, describing something, or showing clear intention. This paper presents an approach to learn the synthesis of human actions and natural language. The generative framework is inspired by Conditional Generative Adversarial Network (CGAN), and it makes use of the Convolutional Neural Network (CNN) with the Action Encoder/Decoder for action representation. The experimental and comparative results verified the efficiency of the proposed approach to produce human actions synthesized with text descriptions. Finally, through the Transformation model, the generated data were converted to a set of joint angles of the target robot, being the robot’s communicative gestures. By employing the generated human-like actions for robots, it suggests that robots’ social cues could be more understandable by humans.
Rights: This is the author's version of the work. Copyright (C) 2020 IEEE. 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2020, pp.201-207. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/16930
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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