  Humans have the fascinating capacity of processing non-verbal visual cues to
understand and anticipate the actions of other humans. This "intention reading"
ability is underpinned by shared motor-repertoires and action-models, which we
use to interpret the intentions of others as if they were our own.
  We investigate how the different cues contribute to the legibility of human
actions during interpersonal interactions. Our first contribution is a publicly
available dataset with recordings of human body-motion and eye-gaze, acquired
in an experimental scenario with an actor interacting with three subjects. From
these data, we conducted a human study to analyse the importance of the
different non-verbal cues for action perception. As our second contribution, we
used the motion/gaze recordings to build a computational model describing the
interaction between two persons. As a third contribution, we embedded this
model in the controller of an iCub humanoid robot and conducted a second human
study, in the same scenario with the robot as an actor, to validate the model's
"intention reading" capability.
  Our results show that it is possible to model (non-verbal) signals exchanged
by humans during interaction, and how to incorporate such a mechanism in
robotic systems with the twin goal of : (i) being able to "read" human action
intentions, and (ii) acting in a way that is legible by humans.
