{"ID":2837107,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.20593","arxiv_id":"2511.20593","title":"Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning","abstract":"Learning safe and stable robot motions from demonstrations remains a challenge, especially in complex, nonlinear tasks involving dynamic, obstacle-rich environments. In this paper, we propose Safe and Stable Neural Network Dynamical Systems S$^2$-NNDS, a learning-from-demonstration framework that simultaneously learns expressive neural dynamical systems alongside neural Lyapunov stability and barrier safety certificates. Unlike traditional approaches with restrictive polynomial parameterizations, S$^2$-NNDS leverages neural networks to capture complex robot motions, providing probabilistic guarantees through split conformal prediction in learned certificates. Experimental results in various 2D and 3D datasets -- including LASA handwriting and demonstrations recorded kinesthetically from the Franka Emika Panda robot -- validate the effectiveness of S$^2$-NNDS in learning robust, safe, and stable motions from potentially unsafe demonstrations. The source code, supplementary material and experiment videos can be accessed via https://github.com/allemmbinn/S2NNDS","short_abstract":"Learning safe and stable robot motions from demonstrations remains a challenge, especially in complex, nonlinear tasks involving dynamic, obstacle-rich environments. In this paper, we propose Safe and Stable Neural Network Dynamical Systems S$^2$-NNDS, a learning-from-demonstration framework that simultaneously learns...","url_abs":"https://arxiv.org/abs/2511.20593","url_pdf":"https://arxiv.org/pdf/2511.20593v2","authors":"[\"Allen Emmanuel Binny\",\"Mahathi Anand\",\"Hugo T. M. Kussaba\",\"Lingyun Chen\",\"Shreenabh Agrawal\",\"Fares J. Abu-Dakka\",\"Abdalla Swikir\"]","published":"2025-11-25T18:24:11Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"eess.SY\"]","methods":"[]","has_code":false,"code_links":[{"ID":606655,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2837107,"paper_url":"https://arxiv.org/abs/2511.20593","paper_title":"Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning","repo_url":"https://github.com/allemmbinn/S2NNDS","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}