将机器人控制隐变量转化为物理模型，实现无需修改策略的在线调优与鲁棒性分析
Interpreting Control Latents for System Identification via Conditional Flow Matching
- 通过条件流匹配解码控制隐变量为四旋翼模型分布
- 在执行器扰动下位置追踪RMSE降低23%，航向误差降低45%
- 适用于固定自适应策略的诊断、调优与鲁棒性评估
隐变量控制策略虽能适应动态变化的机器人系统，但其学习到的隐变量是策略内部表示，而非可观察、可推演或供其他控制模块使用的物理模型，限制了闭环分析、故障诊断和策略优化。直接从隐变量映射到物理参数也存在歧义，因为不同系统可能产生相似闭环行为。为此，本文利用条件流匹配将每个操作隐变量解码为四旋翼模型分布。该解码后的模型分布支持两项下游应用：在不修改策略的前提下，对高层控制器进行基于固定底层策略的在线预测调优；以及在指定扰动下的鲁棒性分析。在执行器动态扰动下，解码模型的预测调优使位置跟踪均方根误差（RMSE）减少23%，航向跟踪误差减少45%；在高斯力扰动下，解码模型集合能精确预测横向跟踪误差演化。结果表明，控制隐变量可被转换为物理模型集合，用于调优、鲁棒性分析和冻结策略的诊断。
原文摘要 · Abstract (English)
Latent-conditioned adaptive policies can control robots across changing dynamics, but their learned latents remain internal representations of the policy rather than physical models that can be inspected, rolled out, or used by other control modules. This limits closed-loop analysis, diagnosis, and further improvement of a fixed policy. A direct mapping from latent to physical parameters is also under-specified, because multiple systems can induce similar closed-loop behavior. We therefore decode each operational latent into a distribution of quadrotor models using conditional flow matching. The decoded distribution enables two downstream uses without modifying the policy: online predictive tuning of a high-level controller around the fixed low-level policy, and robustness analysis under specified disturbances. Under perturbed actuator dynamics, decoded-model predictive tuning reduces position tracking RMSE by $23\%$ and heading RMSE by $45\%$ relative to fixed gains. Under Gaussian force disturbances, decoded-model ensembles closely predict the lateral tracking-error evolution. Together, these results show that control latents can be converted into physical model ensembles for tuning, robustness analysis, and diagnosis of frozen adaptive policies.
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