通过条件流匹配解释系统辨识的控制潜变量
Interpreting Control Latents for System Identification via Conditional Flow Matching
- UC Berkeley（加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出用条件流匹配将控制潜变量解码为四旋翼模型集合，实现了冻结自适应策略的调优与鲁棒分析，使位置跟踪RMSE降23%、航向RMSE降45%。
AI中文摘要：
基于潜变量的自适应策略可在动力学变化时控制机器人，但其学习到的潜变量是策略的内部表示，而非可检查、推演或被其他控制模块使用的物理模型，这限制了对固定策略的闭环分析、诊断与进一步改进；由于多个系统可产生相似的闭环行为，潜变量到物理参数的直接映射也未明确。因此，本研究使用条件流匹配将每个操作潜变量解码为四旋翼模型的分布。该解码分布可在不修改策略的情况下实现两个下游用途：围绕固定底层策略对高层控制器进行在线预测调优，以及在指定扰动下进行鲁棒性分析。在执行器动力学受扰动时，与固定增益相比，解码模型预测调优使位置跟踪均方根误差（RMSE）降低23%，航向RMSE降低45%；在高斯力扰动下，解码模型集合能准确预测横向跟踪误差的演化。综上，这些结果表明，控制潜变量可转换为物理模型集合，用于冻结自适应策略的调优、鲁棒性分析与诊断。
英文摘要：
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.
