微分可能MPCを用いたUAV姿勢制御のためのEnd-to-End自己教師あり学習
Imperative MPC: An End-to-End Self-Supervised Learning with Differentiable MPC for UAV Attitude Control
学習ベースの慣性オドメトリと微分可能モデル予測制御を組み合わせ、UAV姿勢制御を自己教師ありでEnd-to-End学習する枠組みを提案。強風下でも有効で、MPCパラメータとIMU予測性能を同時に改善できる。
著者: Haonan He, Yuheng Qiu, Junyi Geng
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Modeling and control of nonlinear dynamics are critical in robotics, especially in scenarios with unpredictable external influences and complex dynamics. Traditional cascaded modular control pipelines often yield suboptimal performance due to conservative assumptions and tedious parameter tuning. Pure data-driven approaches promise robust performance but suffer from low sample efficiency, sim-to-real gaps, and reliance on extensive datasets. Hybrid methods combining learning-based and traditional model-based control in an end-to-end manner offer a promising alternative. This work presents a self-supervised learning framework combining learning-based inertial odometry (IO) module and differentiable model predictive control (d-MPC) for Unmanned Aerial Vehicle (UAV) attitude control. The IO denoises raw IMU measurements and predicts UAV attitudes, which are then optimized by MPC for control actions in a bi-level optimization (BLO) setup, where the inner MPC optimizes control actions and the upper level minimizes discrepancy between real-world and predicted performance. The framework is thus end-to-end and can be trained in a self-supervised manner. This approach combines the strength of learning-based perception with the interpretable model-based control. Results show the effectiveness even under strong wind. It can simultaneously enhance both the MPC parameter learning and IMU prediction performance.
