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Journal Article Nonverbal Social Behavior Generation for Social Robots Using End-to-End Learning
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Authors
Woo-Ri Ko, Minsu Jang, Jaeyeon Lee, Jaehong Kim
Issue Date
2024-04
Citation
International Journal of Robotics Research, v.43, no.5, pp.716-728
ISSN
0278-3649
Publisher
SAGE Publications
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1177/02783649231207974
Abstract
Social robots facilitate improved human–robot interactions through nonverbal behaviors such as handshakes or hugs. However, the traditional methods, which rely on precoded motions, are predictable and can detract from the perception of robots as interactive agents. To address this issue, we have introduced a Seq2Seq-based neural network model that learns social behaviors from human–human interactions in an end-to-end manner. To mitigate the risk of invalid pose sequences during long-term behavior generation, we incorporated a generative adversarial network (GAN). This proposed method was tested using the humanoid robot, Pepper, in a simulated environment. Given the challenges in assessing the success of social behavior generation, we devised novel metrics to quantify the discrepancy between the generated and ground-truth behaviors. Our analysis reveals the impact of different networks on behavior generation performance and compares the efficacy of learning multiple behaviors versus a single behavior. We anticipate that our method will find application in various sectors, including home service, guide, delivery, educational, and virtual robots, thereby enhancing user interaction and enjoyment.
KSP Keywords
Behavior Generation, End to End(E2E), Generation performance, Long-term behavior, Perception of robots, Simulated Environment, Social behavior, Traditional methods, User interaction, end-to-end learning, generative adversarial network