不確かなシステムのための安全な能動的連続クープマン学習：収縮性保証付き
SACK : Safe Active Continual Koopman Learning for Uncertain Systems with Contractive Guarantees
クープマンモデルをオンラインで安全に適応学習させる枠組みSACKを提案し、収縮則による理論的収束保証と能動学習によるデータ効率向上を実現した。
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著者: Chandan Kumar Sah, Rajpal Singh, Jishnu Keshavan
分類: cs.RO
原文アブストラクト
Koopman operator theory provides a powerful framework for representing nonlinear dynamics through a linear operator acting on lifted observables, enabling the use of linear control techniques for nonlinear systems. However, Koopman models are typically learned from data and often degrade in performance under model uncertainty and distributional shifts between training and deployment. Although several works have explored online adaptation to address this issue, many rely on neural network-based updates that introduce significant computational overhead and lack formal safety guarantees, limiting their suitability for real-time and safety-critical robotic applications. In this work, we propose SACK, a unified framework for continual adaptive Koopman learning that enables safe and efficient online refinement of learned models during task execution. A Koopman model is first learned offline and subsequently refined online through a contractive adaptation law, which provides theoretical convergence guarantees under distributional shifts and model uncertainty. To improve data efficiency and accelerate model refinement, the adaptation mechanism is integrated with an active learning strategy that drives the system to collect informative data while accomplishing task objectives. The resulting control problem is formulated as a nonconvex optimization problem incorporating both active learning objectives and safety constraints. We further derive theoretical bounds on model approximation error and show how these bounds can be incorporated within a robust Model Predictive Control (MPC) framework to provide formal safety guarantees. To reduce conservatism in practice, we also introduce a conformal prediction-based tightening mechanism that calibrates safety margins online from observed residuals. Extensive simulation and experimental studies demonstrate efficacy of the proposed scheme.
