Nov 26, 2022

A deep learning model that generates nonverbal social behavior for robots

Posted by in category: robotics/AI

Researchers at the Electronics and Telecommunications Research Institute (ETRI) in Korea have recently developed a deep learning-based model that could help to produce engaging nonverbal social behaviors, such as hugging or shaking someone’s hand, in robots. Their model, presented in a paper pre-published on arXiv, can actively learn new context-appropriate social behaviors by observing interactions among humans.

“Deep learning techniques have produced interesting results in areas such as computer vision and ,” Woo-Ri Ko, one of the researchers who carried out the study, told TechXplore. “We set out to apply to , specifically by allowing robots to learn from human-human interactions on their own. Our method requires no prior knowledge of human behavior models, which are usually costly and time-consuming to implement.”

The (ANN)-based architecture developed by Ko and his colleagues combines the Seq2Seq (sequence-to-sequence) model introduced by Google researchers in 2014 with generative adversarial networks (GANs). The new architecture was trained on the AIR-Act2Act dataset, a collection of 5,000 human-human interactions occurring in 10 different scenarios.

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