Towards Probabilistic Generative Models for Socially Intelligent Robots

Socially intelligent robots represent a scientific and technological challenge that needs significant contributions at the cross-roads of machine learning, computer vision, audio processing and robotics to be addressed. Naturally, given that the communication modalities most commonly used in human interaction are audio and video, audio-visual fusion plays is a skill to be mastered for robots to exhibit social intelligence. The scenarios and tasks to be addressed in social robotics, require learning methods operating in unsupervised settings and handling the uncertainty in perception and in action. In addition, the developed tools cannot disregard the recent advances and breakthrough in representation learning brought by deep neural architectures. Consequently, combinations of probabilistic models – able to account for uncertainty – and deep neural networks – learning powerful data representations – seem the most appropriate methodological framework to develop social intelligence for robotic platforms. In this manuscript, we motivate the interest of developing machine learning methods exploiting audio-visual data to endow robots with social intelligence. We present the foundations of probabilistic generative models and discuss in detail seven recent contributions. Each of them addresses a different task, useful to acquire social intelligence, and are developed under the large framework of deep probabilistic models – combinations of deep neural networks and probabilistic models. Several future research opportunities as well as my personal scientific ambition for the next years conclude the manuscript.