用可验证方法提升模型外控制的安全性，尤其在数据分布之外仍可靠。
Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis
- 结合置信预测与系统级合成，动态生成高置信误差边界
- 在4维汽车和12维无人机上验证，数据外控制更安全鲁棒
- 适合需要高安全性保障的自动驾驶、机器人等场景
我们提出一种新框架，利用置信预测（CP）与系统级合成（SLS）实现学习动力学模型在训练数据分布外的鲁棒规划与控制，解决模型外使用时的安全性挑战。通过引入基于状态-控制相关协方差模型的加权置信预测，推导出高置信度的模型误差边界，并将其嵌入基于SLS的鲁棒非线性模型预测控制（MPC）中，通过体积优化的前向可达集对预测时域进行约束紧化。理论证明了在分布漂移下的覆盖率与鲁棒性保证，并分析了数据密度与轨迹管尺寸对预测覆盖的影响。实验在复杂度递增的非线性系统上验证，包括4维汽车与12维四旋翼无人机，结果表明相较于固定边界和非鲁棒基线方法，本方法在数据分布外显著提升了安全性和鲁棒性。
原文摘要 · Abstract (English)
We present a novel framework for robust out-of-distribution planning and control using conformal prediction (CP) and system level synthesis (SLS), addressing the challenge of ensuring safety and robustness when using learned dynamics models beyond the training data distribution. We first derive high-confidence model error bounds using weighted CP with a learned, state-control-dependent covariance model. These bounds are integrated into an SLS-based robust nonlinear model predictive control (MPC) formulation, which performs constraint tightening over the prediction horizon via volume-optimized forward reachable sets. We provide theoretical guarantees on coverage and robustness under distributional drift, and analyze the impact of data density and trajectory tube size on prediction coverage. Empirically, we demonstrate our method on nonlinear systems of increasing complexity, including a 4D car and a {12D} quadcopter, improving safety and robustness compared to fixed-bound and non-robust baselines, especially outside of the data distribution.
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