Learning Physical Intuition of Block Towers by Example

Understand

Wooden blocks are a common toy for infants, allowing them to develop motor skills and gain intuition about the physical behavior of the world.

• In this paper, we explore the ability of deep feed-forward models to learn such intuitive physics.
• Using a 3D game engine, we create small towers of wooden blocks whose stability is randomized and render them collapsing (or remaining upright).
• This data allows us to train large convolutional network models which can accurately predict the outcome, as well as estimating the block trajectories.