  In social robotics, endowing humanoid robots with the ability to generate
bodily expressions of affect can improve human-robot interaction and
collaboration, since humans attribute, and perhaps subconsciously anticipate,
such traces to perceive an agent as engaging, trustworthy, and socially
present. Robotic emotional body language needs to be believable, nuanced and
relevant to the context. We implemented a deep learning data-driven framework
that learns from a few hand-designed robotic bodily expressions and can
generate numerous new ones of similar believability and lifelikeness. The
framework uses the Conditional Variational Autoencoder model and a sampling
approach based on the geometric properties of the model's latent space to
condition the generative process on targeted levels of valence and arousal. The
evaluation study found that the anthropomorphism and animacy of the generated
expressions are not perceived differently from the hand-designed ones, and the
emotional conditioning was adequately differentiable between most levels except
the pairs of neutral-positive valence and low-medium arousal. Furthermore, an
exploratory analysis of the results reveals a possible impact of the
conditioning on the perceived dominance of the robot, as well as on the
participants' attention.
