We present a method for sampling-based model predictive control that makes use of a generic physics simulator as the dynamical model. In particular, we propose a Model Predictive Path Integral controller (MPPI), that uses the GPU-parallelizable IsaacGym simulator to compute the forward dynamics of a problem. By doing so, we eliminate the need for explicit encoding of robot dynamics and contacts with objects for MPPI. Since no explicit dynamic modeling is required, our method is easily extendable to different objects and robots and allows one to solve complex navigation and contact-rich tasks. We demonstrate the effectiveness of this method in several simulated and real-world settings, among which mobile navigation with collision avoidance, non-prehensile manipulation, and whole-body control for high-dimensional configuration spaces. This method is a powerful and accessible open-source tool to solve a large variety of contact-rich motion planning tasks.

翻译：我们提出一种基于采样的模型预测控制方法，该方法采用通用物理仿真器作为动力学模型。具体而言，我们设计了一种模型预测路径积分控制器（MPPI），利用支持GPU并行化的IsaacGym仿真器计算问题的前向动力学。通过这种方式，我们无需为MPPI显式编码机器人动力学及其与物体的接触关系。由于无需显式动力学建模，该方法可轻松扩展至不同物体与机器人，并支持解决复杂导航及富含接触的任务。我们在多个仿真与真实场景中验证了该方法的有效性，包括带避障的移动导航、非抓取式操作以及高维配置空间下的全身控制。该方法是一种强大且易于获取的开源工具，可解决各类富含接触的运动规划任务。