Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative Adversarial Neural Networks

TL;DR

This paper presents a novel approach using generative adversarial neural networks to forecast nonverbal social signals during dyadic interactions, aiming to improve social robot interactions by modeling human nonverbal behaviors.

Contribution

It introduces a new method for predicting human nonverbal cues in social interactions using GANs, facilitating more humanlike robot behavior.

Findings

Demonstrates effective prediction of gaze and gesture signals

Improves robot-human interaction transparency

Enhances social robot responsiveness

Abstract

We are approaching a future where social robots will progressively become widespread in many aspects of our daily lives, including education, healthcare, work, and personal use. All of such practical applications require that humans and robots collaborate in human environments, where social interaction is unavoidable. Along with verbal communication, successful social interaction is closely coupled with the interplay between nonverbal perception and action mechanisms, such as observation of gaze behaviour and following their attention, coordinating the form and function of hand gestures. Humans perform nonverbal communication in an instinctive and adaptive manner, with no effort. For robots to be successful in our social landscape, they should therefore engage in social interactions in a humanlike way, with increasing levels of autonomy. In particular, nonverbal gestures are expected to endow social robots with the capability of emphasizing their speech, or showing their intentions. Motivated by this, our research sheds a light on modeling human behaviors in social interactions, specifically, forecasting human nonverbal social signals during dyadic interactions, with an overarching goal of developing robotic interfaces that can learn to imitate human dyadic interactions. Such an approach will ensure the messages encoded in the robot gestures could be perceived by interacting partners in a facile and transparent manner, which could help improve the interacting partner perception and makes the social interaction outcomes enhanced.