  Building embodied autonomous agents capable of participating in social
interactions with humans is one of the main challenges in AI. This problem
motivated many research directions on embodied language use. Current approaches
focus on language as a communication tool in very simplified and non diverse
social situations: the "naturalness" of language is reduced to the concept of
high vocabulary size and variability. In this paper, we argue that aiming
towards human-level AI requires a broader set of key social skills: 1) language
use in complex and variable social contexts; 2) beyond language, complex
embodied communication in multimodal settings within constantly evolving social
worlds. In this work we explain how concepts from cognitive sciences could help
AI to draw a roadmap towards human-like intelligence, with a focus on its
social dimensions. We then study the limits of a recent SOTA Deep RL approach
when tested on a first grid-world environment from the upcoming SocialAI, a
benchmark to assess the social skills of Deep RL agents. Videos and code are
available at https://sites.google.com/view/socialai01 .
