让机器人实时控制更快更稳，用反馈机制避免重复计算。
Feedback-MPPI: Fast Sampling-Based MPC via Rollout Differentiation -- Adios low-level controllers
- 通过敏感性分析计算局部反馈增益，实现快速闭环修正。
- 在真实四足与无人机上实现高频稳定控制，性能显著提升。
- 适合需要高速响应的复杂机器人系统，如越野运动、特技飞行。
模型预测路径积分控制是一种强大的基于采样的方法，因其对非线性动力学和非凸代价函数的灵活性，适用于复杂的机器人任务。然而，其在实时高频机器人控制场景中的应用受限于计算开销。本文提出反馈-路径积分（F-MPPI）框架，通过受李卡提反馈启发的灵敏度分析，计算局部线性反馈增益，从而在不重新优化的情况下实现对当前状态的快速闭环修正。我们在两个机器人平台上验证了F-MPPI的有效性：一个四足机器人在不平坦地形上执行动态步态，以及一架无人机构建于机载计算平台，完成高难度机动。结果表明，引入局部反馈显著提升了控制性能与稳定性，实现了适合复杂机器人的鲁棒高频运行。
原文摘要 · Abstract (English)
Model Predictive Path Integral control is a powerful sampling-based approach suitable for complex robotic tasks due to its flexibility in handling nonlinear dynamics and non-convex costs. However, its applicability in real-time, highfrequency robotic control scenarios is limited by computational demands. This paper introduces Feedback-MPPI (F-MPPI), a novel framework that augments standard MPPI by computing local linear feedback gains derived from sensitivity analysis inspired by Riccati-based feedback used in gradient-based MPC. These gains allow for rapid closed-loop corrections around the current state without requiring full re-optimization at each timestep. We demonstrate the effectiveness of F-MPPI through simulations and real-world experiments on two robotic platforms: a quadrupedal robot performing dynamic locomotion on uneven terrain and a quadrotor executing aggressive maneuvers with onboard computation. Results illustrate that incorporating local feedback significantly improves control performance and stability, enabling robust, high-frequency operation suitable for complex robotic systems.
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