用Transformer加速机器人轨迹优化，提升收敛速度与效率。
Transformer-based Model Predictive Control: Trajectory Optimization via Sequence Modeling
- 将Transformer作为初始轨迹猜测，嵌入传统优化流程中。
- 实测可提速7倍，迭代次数减少45%，性能不降。
- 适合需要快速实时控制的机器人系统开发者。
模型预测控制（MPC）是处理约束控制的主流方法，广泛应用于各类机器人自主任务。然而，多数实际问题需递归求解高度非凸的轨迹优化问题，导致计算复杂度高且对初值敏感。本文提出一种统一框架，融合优化方法与学习方法的优势：在轨迹生成过程中引入基于Transformer的高容量神经网络，为非凸优化提供近似最优的初始猜测或目标规划。仿真与真实世界实验（搭载于自由飞行平台）表明，该方法显著提升MPC收敛性与运行效率。相比纯优化方法，本方案可实现轨迹生成性能最高提升75%，求解器迭代次数减少最多45%，整体MPC运行时间缩短7倍，且性能无损失。
原文摘要 · Abstract (English)
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.
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