Curiosity-driven Intuitive Physics Learning

TL;DR

This paper introduces a curiosity-driven learning model for AI agents that mimics how infants learn about physical objects through multisensory interactions, focusing on discontinuities in physical properties.

Contribution

It proposes a novel curiosity-based framework that leverages observations of physical discontinuities to facilitate intuitive physics learning in real-world AI agents.

Findings

Model emulates infant-like physical understanding

Supports learning from scratch through experience

Focuses on physical property discontinuities

Abstract

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.