Research in cognitive science has provided extensive evidence of human
cognitive ability in performing physical reasoning of objects from noisy
perceptual inputs. Such a cognitive ability is commonly known as intuitive
physics. With advancements in deep learning, there is an increasing interest in
building intelligent systems that are capable of performing physical reasoning
from a given scene for the purpose of building better AI systems. As a result,
many contemporary approaches in modelling intuitive physics for machine
cognition have been inspired by literature from cognitive science. Despite the
wide range of work in physical reasoning for machine cognition, there is a
scarcity of reviews that organize and group these deep learning approaches.
Especially at the intersection of intuitive physics and artificial
intelligence, there is a need to make sense of the diverse range of ideas and
approaches. Therefore, this paper presents a comprehensive survey of recent
advances and techniques in intuitive physics-inspired deep learning approaches
for physical reasoning. The survey will first categorize existing deep learning
approaches into three facets of physical reasoning before organizing them into
three general technical approaches and propose six categorical tasks of the
field. Finally, we highlight the challenges of the current field and present
some future research directions.

本文综述了基于直觉物理的深度学习方法在物理推理方面的最新进展和技术。将现有的深度学习技术分为三个方面的物理推理，并将其组织为三种一般的技术方法，提出了该领域的六个分类任务。最后，本文强调了当前领域的挑战并提出了一些未来的研究方向。