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 manual encoding of robot dynamics and interactions among objects and allow one to effortlessly solve complex navigation and contact-rich tasks. Since no explicit dynamic modeling is required, the method is easily extendable to different objects and robots. 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 tool to solve a large variety of contact-rich motion planning tasks.

翻译：我们提出了一种基于采样的模型预测控制方法，该方法利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），该控制器借助支持GPU并行化的IsaacGym仿真器来计算问题前向动力学。通过这种方式，我们无需手动编码机器人动力学及物体间的相互作用，即可轻松解决复杂的导航与密集接触任务。由于无需显式动力学建模，该方法能便捷地扩展至不同物体和机器人。我们在多种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、非抓取操作以及高维构型空间的全身控制。该方法为解决各类密集接触运动规划任务提供了强大且易于使用的工具。