Granular materials are of critical interest to many robotic tasks in planetary science, construction, and manufacturing. However, the dynamics of granular materials are complex and often computationally very expensive to simulate. We propose a set of methodologies and a system for the fast simulation of granular materials on Graphics Processing Units (GPUs), and show that this simulation is fast enough for basic training with Reinforcement Learning algorithms, which currently require many dynamics samples to achieve acceptable performance. Our method models granular material dynamics using implicit timestepping methods for multibody rigid contacts, as well as algorithmic techniques for efficient parallel collision detection between pairs of particles and between particle and arbitrarily shaped rigid bodies, and programming techniques for minimizing warp divergence on Single-Instruction, Multiple-Thread (SIMT) chip architectures. We showcase our simulation system on several environments targeted toward robotic tasks, and release our simulator as an open-source tool.

翻译：颗粒材料在行星科学、建筑和制造领域的众多机器人任务中具有关键意义。然而，颗粒材料的动力学特性复杂，且仿真计算成本通常极高。我们提出了一套在图形处理器（GPU）上快速仿真颗粒材料的方法体系和系统，并证明该仿真速度足以满足基于强化学习算法的基本训练需求（当前此类算法需大量动力学样本来达到可接受的性能）。我们的方法采用多体刚性接触的隐式时间步进方法来建模颗粒材料动力学，同时运用高效并行碰撞检测算法技术（涵盖颗粒对之间以及颗粒与任意形状刚体之间的碰撞），并结合编程技术以最小化单指令多线程（SIMT）芯片架构上的线程束发散。我们在多个面向机器人任务的环境中展示了仿真系统，并以开源工具形式发布了该仿真器。