GPU並列物理シミュレーションを活用したサンプリングベースモデル予測制御
Sampling-based Model Predictive Control Leveraging Parallelizable Physics Simulations
GPU並列化可能なIsaacGymを動力学モデルとして用いるMPPIを提案し、明示的な動力学モデリングなしに接触の多い複雑な運動計画タスクを実現した。
著者: Corrado Pezzato, Chadi Salmi, Elia Trevisan, Max Spahn, Javier Alonso-Mora, Carlos Hernández Corbato
分類: cs.RO
原文アブストラクト
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.
