用自监督残差学习优化飞行器轨迹，让控制更精准
Optimizing Control-Friendly Trajectories with Self-Supervised Residual Learning
- 通过轨迹数据学习未知动力学作为残差，构建混合模型
- 实现长时程高精度预测，支持任意积分步长
- 适合需要高动态飞行的无人机控制优化
现代复杂机器人系统的真实物理难以精确解析建模，导致在控制器设计中跟踪激进轨迹时存在残差动力学，影响控制精度。本文提出一种自监督残差学习与轨迹优化框架：首先，将闭环模型中的未知动态效应作为名义动力学的残差进行学习，形成混合模型；通过仅使用轨迹级数据，即可实现基于解析梯度的学习，并支持任意积分步长下的准确长时程预测。随后，开发轨迹优化器，在优化过程中最小化轨迹上的残差物理量，生成对后续控制友好的最优参考轨迹。四旋翼的敏捷飞行验证表明，利用该混合动力学模型，所提优化器可生成可被精确跟踪的激进运动轨迹。
原文摘要 · Abstract (English)
Real-world physics can only be analytically modeled with a certain level of precision for modern intricate robotic systems. As a result, tracking aggressive trajectories accurately could be challenging due to the existence of residual physics during controller synthesis. This paper presents a self-supervised residual learning and trajectory optimization framework to address the aforementioned challenges. At first, unknown dynamic effects on the closed-loop model are learned and treated as residuals of the nominal dynamics, jointly forming a hybrid model. We show that learning with analytic gradients can be achieved using only trajectory-level data while enjoying accurate long-horizon prediction with an arbitrary integration step size. Subsequently, a trajectory optimizer is developed to compute the optimal reference trajectory with the residual physics along it minimized. It ends up with trajectories that are friendly to the following control level. The agile flight of quadrotors illustrates that by utilizing the hybrid dynamics, the proposed optimizer outputs aggressive motions that can be precisely tracked.
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