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.

物理推理是开发通用人工智能系统的关键因素，本论文旨在提供现有基准的综述及其解决方法，并提出了衡量人工智能系统物理推理能力的统一观点。我们选择了设计用于测试物理推理任务中算法性能的基准。虽然每个选定的基准都提出了独特的挑战，但它们的集合为具有各种物理推理概念可测量技能水平的通用 AI 代理提供了全面的实践场所。因此，这些基准集合相较于旨在模拟现实世界并交织其复杂性和许多概念的综合基准具有优势。我们将所呈现的一组物理推理基准分为子类别，以首先在这些组上对更窄的通用 AI 代理进行测试。