Natural languages enable humans to engage in highly complex social and conversational interactions with each other. Alife approaches to the origins and emergence of language typically manage this complexity by carefully staging the learning paths that embodied artificial agents need to follow in order to bootstrap their own communication system from scratch. This paper investigates how these scaffolds introduced by the experimenter can be removed by allowing agents to autonomously set their own challenges when they are driven by intrinsic motivation and have the capacity to self-assess their own skills at achieving their communicative goals. The results suggest that intrinsic motivation not only allows agents to spontaneously develop their own learning paths, but also that they are able to make faster transitions from one learning phase to the next.