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. The models are also able to generalize in two important ways: (i)
to new physical scenarios, e.g. towers with an additional block and (ii) to
images of real wooden blocks, where it obtains a performance comparable to
human subjects.

探索深度前馈模型对婴幼儿木制积木玩具的直觉物理学习能力的研究，并运用三维游戏引擎创建稳定性随机的木制积木小塔并在其坍塌或保持竖直状态的情况下识别积木轨迹，训练大型卷积神经网络模型以准确预测结果，及在新的物理场景和真实木质积木的图像中进行泛化。

通过示例学习积木塔的物理直觉

Learning Physical Intuition of Block Towers by Example

A key challenge in designing convolutional network models is sizing them
appropriately. Many factors are involved in these decisions, including number
of layers, feature maps, kernel sizes, etc. Complicating this further is the
fact that each of these influence not only the numbers and dimensions of the
activation units, but also the total number of parameters. In this paper we
focus on assessing the independent contributions of three of these linked
variables: The numbers of layers, feature maps, and parameters. To accomplish
this, we employ a recursive convolutional network whose weights are tied
between layers; this allows us to vary each of the three factors in a
controlled setting. We find that while increasing the numbers of layers and
parameters each have clear benefit, the number of feature maps (and hence
dimensionality of the representation) appears ancillary, and finds most of its
benefit through the introduction of more weights. Our results (i) empirically
confirm the notion that adding layers alone increases computational power,
within the context of convolutional layers, and (ii) suggest that precise
sizing of convolutional feature map dimensions is itself of little concern;
more attention should be paid to the number of parameters in these layers
instead.

本文考察卷积神经网络中层数、特征图数量和参数数量对模型性能的影响，结果表明，增加层数和参数数量都有明显改善，而特征图数量（和表示的维度）对模型性能影响较小，更应关注这些层中的参数数量。