  Physical reasoning is a crucial aspect in the development of general AI
systems, given that human learning starts with interacting with the physical
world before progressing to more complex concepts. Although researchers have
studied and assessed the physical reasoning of AI approaches through various
specific benchmarks, there is no comprehensive approach to evaluating and
measuring progress. Therefore, we aim to offer an overview of existing
benchmarks and their solution approaches and propose a unified perspective for
measuring the physical reasoning capacity of AI systems. We select benchmarks
that are designed to test algorithmic performance in physical reasoning tasks.
While each of the selected benchmarks poses a unique challenge, their ensemble
provides a comprehensive proving ground for an AI generalist agent with a
measurable skill level for various physical reasoning concepts. This gives an
advantage to such an ensemble of benchmarks over other holistic benchmarks that
aim to simulate the real world by intertwining its complexity and many
concepts. We group the presented set of physical reasoning benchmarks into
subcategories so that more narrow generalist AI agents can be tested first on
these groups.
