Phy-Q as a measure for physical reasoning intelligence

TL;DR

This paper introduces Phy-Q, a new benchmark for measuring physical reasoning intelligence in AI agents through diverse scenarios, revealing current agents' significant performance gap compared to humans.

Contribution

The paper presents a novel testbed with physical scenarios and a scoring metric, enabling evaluation of physical reasoning and generalization in AI agents.

Findings

All tested agents perform below human levels.

Learning agents struggle with physical rule generalization.

Current agents show limited physical reasoning capabilities.

Abstract

Humans are well-versed in reasoning about the behaviors of physical objects and choosing actions accordingly to accomplish tasks, while it remains a major challenge for AI. To facilitate research addressing this problem, we propose a new testbed that requires an agent to reason about physical scenarios and take an action appropriately. Inspired by the physical knowledge acquired in infancy and the capabilities required for robots to operate in real-world environments, we identify 15 essential physical scenarios. We create a wide variety of distinct task templates, and we ensure all the task templates within the same scenario can be solved by using one specific strategic physical rule. By having such a design, we evaluate two distinct levels of generalization, namely the local generalization and the broad generalization. We conduct an extensive evaluation with human players, learning agents with varying input types and architectures, and heuristic agents with different strategies. Inspired by how human IQ is calculated, we define the physical reasoning quotient (Phy-Q score) that reflects the physical reasoning intelligence of an agent using the physical scenarios we considered. Our evaluation shows that 1) all agents are far below human performance, and 2) learning agents, even with good local generalization ability, struggle to learn the underlying physical reasoning rules and fail to generalize broadly. We encourage the development of intelligent agents that can reach the human level Phy-Q score. Website: https://github.com/phy-q/benchmark