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