Interpreting Control Latents for System Identification via Conditional Flow Matching

2026-08-24

AI Summary

本文通过条件流匹配方法将控制潜变量解码为四旋翼模型分布，以实现固定策略的在线预测调优和鲁棒性分析。

Abstract

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.

Problem

• latent-conditioned adaptive policies
• physical models
• system identification

Innovation

• conditional flow matching
• control latents
• system identification
• predictive tuning
• robustness analysis