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

Phy-Q as a measure for physical reasoning intelligence