{"ID":2891120,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.17096","arxiv_id":"2507.17096","title":"ZORMS-LfD: Learning from Demonstrations with Zeroth-Order Random Matrix Search","abstract":"We propose Zeroth-Order Random Matrix Search for Learning from Demonstrations (ZORMS-LfD). ZORMS-LfD enables the costs, constraints, and dynamics of constrained optimal control problems, in both continuous and discrete time, to be learned from expert demonstrations without requiring smoothness of the learning-loss landscape. In contrast, existing state-of-the-art first-order methods require the existence and computation of gradients of the costs, constraints, dynamics, and learning loss with respect to states, controls and/or parameters. Most existing methods are also tailored to discrete time, with constrained problems in continuous time receiving only cursory attention. We demonstrate that ZORMS-LfD matches or surpasses the performance of state-of-the-art methods in terms of both learning loss and compute time across a variety of benchmark problems. On unconstrained continuous-time benchmark problems, ZORMS-LfD achieves similar loss performance to state-of-the-art first-order methods with an over $80$\\% reduction in compute time. On constrained continuous-time benchmark problems where there is no specialized state-of-the-art method, ZORMS-LfD is shown to outperform the commonly used gradient-free Nelder-Mead optimization method.","short_abstract":"We propose Zeroth-Order Random Matrix Search for Learning from Demonstrations (ZORMS-LfD). ZORMS-LfD enables the costs, constraints, and dynamics of constrained optimal control problems, in both continuous and discrete time, to be learned from expert demonstrations without requiring smoothness of the learning-loss land...","url_abs":"https://arxiv.org/abs/2507.17096","url_pdf":"https://arxiv.org/pdf/2507.17096v1","authors":"[\"Olivia Dry\",\"Timothy L. Molloy\",\"Wanxin Jin\",\"Iman Shames\"]","published":"2025-07-23T00:23:01Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"eess.SY\",\"math.NA\",\"math.OC\"]","methods":"[]","has_code":false}