Data-driven emotional body language generation for social robotics
This addresses the problem of improving human-robot interaction by making robots appear more engaging and trustworthy, though it is incremental as it builds on existing methods for emotional expression generation.
The paper tackled generating believable emotional body language for social robots using a deep learning framework, achieving generated expressions perceived as similarly anthropomorphic and animacy as hand-designed ones, with emotional conditioning mostly differentiable except for neutral-positive valence and low-medium arousal pairs.
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.