  Biological infants are naturally curious and try to comprehend their physical
surroundings by interacting, in myriad multisensory ways, with different
objects - primarily macroscopic solid objects - around them. Through their
various interactions, they build hypotheses and predictions, and eventually
learn, infer and understand the nature of the physical characteristics and
behavior of these objects. Inspired thus, we propose a model for
curiosity-driven learning and inference for real-world AI agents. This model is
based on the arousal of curiosity, deriving from observations along
discontinuities in the fundamental macroscopic solid-body physics parameters,
i.e., shape constancy, spatial-temporal continuity, and object permanence. We
use the term body-budget to represent the perceived fundamental properties of
solid objects. The model aims to support the emulation of learning from scratch
followed by substantiation through experience, irrespective of domain, in
real-world AI agents.
