To compute robust 2D assembly plans, we present an approach that combines geometric planning with a deep neural network. We train the network using the Box2D physics simulator with added stochastic noise to yield robustness scores--the success probabilities of planned assembly motions. As running a simulation for every assembly motion is impractical, we train a convolutional neural network to map assembly operations, given as an image pair of the subassemblies before and after they are mated, to a robustness score. The neural network prediction is used within a planner to quickly prune out motions that are not robust. We demonstrate this approach on two-handed planar assemblies, where the motions are one-step translations. Results suggest that the neural network can learn robustness to plan robust sequences an order of magnitude faster than physics simulation.

中文翻译：

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通过具有学习分数的几何规划进行稳健的 2D 装配排序

为了计算稳健的 2D 装配计划，我们提出了一种将几何规划与深度神经网络相结合的方法。我们使用 Box2D 物理模拟器训练网络，并添加随机噪声以产生稳健性分数——计划装配运动的成功概率。由于对每个装配运动进行模拟是不切实际的，我们训练了一个卷积神经网络来将装配操作映射到一个稳健性分数，这些操作作为装配前后的子装配的图像对给出。在规划器中使用神经网络预测来快速修剪不稳健的运动。我们在双手平面组件上演示了这种方法，其中运动是一步平移。