Model predictive control (MPC) has established itself as the primary methodology for constrained control, enabling general-purpose robot autonomy in diverse real-world scenarios. However, for most problems of interest, MPC relies on the recursive solution of highly non-convex trajectory optimization problems, leading to high computational complexity and strong dependency on initialization. In this work, we present a unified framework to combine the main strengths of optimization-based and learning-based methods for MPC. Our approach entails embedding high-capacity, transformer-based neural network models within the optimization process for trajectory generation, whereby the transformer provides a near-optimal initial guess, or target plan, to a non-convex optimization problem. Our experiments, performed in simulation and the real world onboard a free flyer platform, demonstrate the capabilities of our framework to improve MPC convergence and runtime. Compared to purely optimization-based approaches, results show that our approach can improve trajectory generation performance by up to 75%, reduce the number of solver iterations by up to 45%, and improve overall MPC runtime by 7x without loss in performance.

翻译：模型预测控制（MPC）已成为约束控制的主要方法，能够在多样化的现实场景中实现通用机器人自主性。然而，对于大多数重要问题，MPC依赖于对高度非凸轨迹优化问题的递归求解，导致计算复杂度高且对初始化具有强依赖性。在本研究中，我们提出了一个统一框架，以结合基于优化和基于学习的MPC方法的主要优势。我们的方法将高容量、基于Transformer的神经网络模型嵌入轨迹生成的优化过程中，通过Transformer为非凸优化问题提供接近最优的初始猜测或目标规划。我们在仿真和自由飞行器平台上的真实世界实验中验证了该框架提升MPC收敛性和运行时的能力。与纯基于优化的方法相比，结果表明我们的方法可将轨迹生成性能提升高达75%，将求解器迭代次数减少高达45%，并在不损失性能的情况下将整体MPC运行时提升7倍。