Scaffolded acquisition of a robot self–world distinction in a layered architecture model of the developing self

How does the sense of self emerge in human development, and could an artificial system acquire a similar sense using algorithmic principles and given a suitable embodiment and experience? Here, we describe a machine learning framework, inspired by developmental psychology and neurobiology, that acquires a self–world distinction and minimal self-model. Unlike previous approaches, we introduce a gradual formulation, integrating correlation-based and predictive techniques within a layered architecture. Using a simulated humanoid, we show first how a self–world distinction can emerge through correlation-based self-supervision. This model uses dependencies between proprioception and vision to distinguish its own body from its surroundings during infant-like motor babbling. Next, we construct a model of how these sensory signals will evolve in time. This predictive capacity uses the self–world distinction acquired by the lower layer as input to neural ordinary differential equation models that learn continuous dynamics. Finally, we demonstrate scaffolding of the higher layer by the lower one, showing that predictive learning occurs more rapidly with the lower layer already in place. By integrating these mechanisms, our approach offers a framework for characterizing a minimal robotic self that can inform our understanding of the emergence of the sense of self in the human mind/brain. This article is part of the discussion meeting issue ‘From being to identity: analysis and synthesis of the self’.