Adversarially Robust Multitask Adaptive Control

• URL: http://arxiv.org/abs/2511.05444v1
• Date: Fri, 07 Nov 2025 17:25:21 GMT
• Title: Adversarially Robust Multitask Adaptive Control
• Authors: Kasra Fallah, Leonardo F. Toso, James Anderson,
• Abstract summary: We study adversarially robust multitask adaptive linear quadratic control.<n>We propose a clustered multitask approach that integrates clustering and system identification with resilient aggregation to mitigate corrupted model updates.
• Score: 6.576173998482649
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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