Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912868004265984 |
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| author | Binny, Allen Emmanuel Anand, Mahathi Kussaba, Hugo T. M. Chen, Lingyun Agrawal, Shreenabh Abu-Dakka, Fares J. Swikir, Abdalla |
| author_facet | Binny, Allen Emmanuel Anand, Mahathi Kussaba, Hugo T. M. Chen, Lingyun Agrawal, Shreenabh Abu-Dakka, Fares J. Swikir, Abdalla |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20593 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning Binny, Allen Emmanuel Anand, Mahathi Kussaba, Hugo T. M. Chen, Lingyun Agrawal, Shreenabh Abu-Dakka, Fares J. Swikir, Abdalla Robotics Systems and Control 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 |
| title | Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2511.20593 |