论文标题
通过推理路径来解决物理难题
Solving Physics Puzzles by Reasoning about Paths
论文作者
论文摘要
我们为目标驱动的任务提出了一个新的深度学习模型，该模型需要直观的物理推理和在场景中进行干预才能实现所需的最终目标。它的模块化结构是通过假设人类试图解决此类任务时采用的一系列直观步骤来激发的。该模型首先预测目标对象不会在没有干预的情况下遵循的路径，并且目标对象应遵循的路径以解决任务。接下来，它预测了动作对象的所需路径并生成动作对象的放置。该模型的所有组成部分均以监督方式共同培训；每个组件都会收到自己的学习信号，但学习信号也通过整个架构进行了反向传播。为了评估模型，我们使用Phyre-在2D力学难题中用于目标驱动物理推理的基准测试。
We propose a new deep learning model for goal-driven tasks that require intuitive physical reasoning and intervention in the scene to achieve a desired end goal. Its modular structure is motivated by hypothesizing a sequence of intuitive steps that humans apply when trying to solve such a task. The model first predicts the path the target object would follow without intervention and the path the target object should follow in order to solve the task. Next, it predicts the desired path of the action object and generates the placement of the action object. All components of the model are trained jointly in a supervised way; each component receives its own learning signal but learning signals are also backpropagated through the entire architecture. To evaluate the model we use PHYRE - a benchmark test for goal-driven physical reasoning in 2D mechanics puzzles.
