{"ID":2898102,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.03992","arxiv_id":"2507.03992","title":"Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems","abstract":"Learning from Demonstration (LfD) techniques enable robots to learn and generalize tasks from user demonstrations, eliminating the need for coding expertise among end-users. One established technique to implement LfD in robots is to encode demonstrations in a stable Dynamical System (DS). However, finding a stable dynamical system entails solving an optimization problem with bilinear matrix inequality (BMI) constraints, a non-convex problem which, depending on the number of scalar constraints and variables, demands significant computational resources and is susceptible to numerical issues such as floating-point errors. To address these challenges, we propose a novel compositional approach that enhances the applicability and scalability of learning stable DSs with BMIs.","short_abstract":"Learning from Demonstration (LfD) techniques enable robots to learn and generalize tasks from user demonstrations, eliminating the need for coding expertise among end-users. One established technique to implement LfD in robots is to encode demonstrations in a stable Dynamical System (DS). However, finding a stable dyna...","url_abs":"https://arxiv.org/abs/2507.03992","url_pdf":"https://arxiv.org/pdf/2507.03992v1","authors":"[\"Shreenabh Agrawal\",\"Hugo T. M. Kussaba\",\"Lingyun Chen\",\"Allen Emmanuel Binny\",\"Abdalla Swikir\",\"Pushpak Jagtap\",\"Sami Haddadin\"]","published":"2025-07-05T10:45:01Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"eess.SY\"]","methods":"[]","has_code":false}