Emotional body language (or EBL for short)
is crucial in human communication
so it comes as no surprise that it is also
important in social HRI too.
Our goal is to propose an automatic method
to generate numerous robotic EBL animations
of high granularity and believability, for
robot specific morphology and kinetics.
Furthermore, we want to be able to generate
animations of targeted valence and arousal.
In our previous work, we used a deep Variational
Autoencoder network
which was trained with a small set of EBL
motion sequences,
specifically designed for a Pepper robot by
professional animators.
However, the valence and arousal of the generated
expressions was not controlled.
In our current work, we solve this by using
a Conditional Variational Autoencoder.
Valence conditioning is concatenated to the
input (that is the c block in the graph),
while arousal was modeled as a parameter for
sampling the latent space of the model.
To boost expressiveness even further, this
time
we train the net with eye LED color sequences
concatenated with motion.
We tested how distinguishable the emotion
conditioning is with 20 participants who watched
18 generated animation.
For valence conditioning, on the left, animations
generated as positive or neutral
received significantly higher valence ratings
compared to animations generated as negative.
For arousal conditioning, on the right, animations
generated as low or medium arousal
were given significantly lower arousal ratings
compared to animations generated as high arousal.
