{"ID":2845025,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.05444","arxiv_id":"2511.05444","title":"Adversarially Robust Multitask Adaptive Control","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.05444","url_pdf":"https://arxiv.org/pdf/2511.05444v1","authors":"[\"Kasra Fallah\",\"Leonardo F. Toso\",\"James Anderson\"]","published":"2025-11-07T17:25:21Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"eess.SY\",\"math.OC\"]","methods":"[]","has_code":false}