Current evaluation protocols predominantly assess physical reasoning in
stationary scenes, creating a gap in evaluating agents' abilities to interact
with dynamic events. While contemporary methods allow agents to modify initial
scene configurations and observe consequences, they lack the capability to
interact with events in real time. To address this, we introduce I-PHYRE, a
framework that challenges agents to simultaneously exhibit intuitive physical
reasoning, multi-step planning, and in-situ intervention. Here, intuitive
physical reasoning refers to a quick, approximate understanding of physics to
address complex problems; multi-step denotes the need for extensive sequence
planning in I-PHYRE, considering each intervention can significantly alter
subsequent choices; and in-situ implies the necessity for timely object
manipulation within a scene, where minor timing deviations can result in task
failure. We formulate four game splits to scrutinize agents' learning and
generalization of essential principles of interactive physical reasoning,
fostering learning through interaction with representative scenarios. Our
exploration involves three planning strategies and examines several supervised
and reinforcement agents' zero-shot generalization proficiency on I-PHYRE. The
outcomes highlight a notable gap between existing learning algorithms and human
performance, emphasizing the imperative for more research in enhancing agents
with interactive physical reasoning capabilities. The environment and baselines
will be made publicly available.

为了解决现有学习算法与人类表现之间的差距并强调增强代理人的交互式物理推理能力的重要性，我们介绍了 I-PHYRE 框架，该框架要求代理人同时展示直觉的物理推理、多步规划和原位干预，通过代表性场景的互动促进学习。