We present a learning-based dynamics model for granular material
manipulation. Inspired by the Eulerian approach commonly used in fluid
dynamics, our method adopts a fully convolutional neural network that operates
on a density field-based representation of object piles and pushers, allowing
it to exploit the spatial locality of inter-object interactions as well as the
translation equivariance through convolution operations. Furthermore, our
differentiable action rendering module makes the model fully differentiable and
can be directly integrated with a gradient-based trajectory optimization
algorithm. We evaluate our model with a wide array of piles manipulation tasks
both in simulation and real-world experiments and demonstrate that it
significantly exceeds existing latent or particle-based methods in both
accuracy and computation efficiency, and exhibits zero-shot generalization
capabilities across various environments and tasks.

基于学习的动力学模型用于颗粒材料操纵，通过卷积神经网络实现了对物体堆和推动者的密度场表示，结合可微分的操作渲染模块，与基于梯度的轨迹优化算法进行直接集成，通过在模拟和实验中验证表明其在准确性和计算效率方面明显超越现有的基于潜变量或基于粒子的方法，并在各种环境和任务中展示了零样本泛化能力。