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arXiv · 2511.05444

Adversarially Robust Multitask Adaptive Control

Kasra Fallah·Leonardo F. Toso·James Anderson
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Abstract

We study adversarially robust multitask adaptive linear quadratic control; a setting where multiple systems collaboratively learn control policies under model uncertainty and adversarial corruption. We propose a clustered multitask approach that integrates clustering and system identification with resilient aggregation to mitigate corrupted model updates. Our analysis characterizes how clustering accuracy, intra-cluster heterogeneity, and adversarial behavior affect the expected regret of certainty-equivalent (CE) control across LQR tasks. We establish non-asymptotic bounds demonstrating that the regret decreases inversely with the number of honest systems per cluster and that this reduction is preserved under a bounded fraction of adversarial systems within each cluster.

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Kasra Fallah, Leonardo F. Toso, James Anderson. 2025-11-07. Adversarially Robust Multitask Adaptive Control. https://arxiv.org/abs/2511.05444

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