経路推論による物理パズルの解法
Solving Physics Puzzles by Reasoning about Paths
目標物体の自然な経路と目標経路を予測し、操作物体の配置を生成するモジュール型深層学習モデルを提案し、2D物理パズルベンチマークPHYREで評価した。
著者: Augustin Harter, Andrew Melnik, Gaurav Kumar, Dhruv Agarwal, Animesh Garg, Helge Ritter
分類: cs.AI, cs.LG, cs.RO
原文アブストラクト
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.
