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Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning
Binny, Allen Emmanuel Anand, Mahathi Kussaba, Hugo TM Chen, Lingyun Agrawal, Shreenabh Abu-Dakka, Fares J Swikir, Abdalla
Binny, Allen Emmanuel
Anand, Mahathi
Kussaba, Hugo TM
Chen, Lingyun
Agrawal, Shreenabh
Abu-Dakka, Fares J
Swikir, Abdalla
Supervisor
Department
Robotics
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Journal article
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http://creativecommons.org/licenses/by/4.0/
Language
English
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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.
Citation
A.E. Binny, M. Anand, H.T.M. Kussaba, L. Chen, S. Agrawal, F.J. Abu-Dakka, A. Swikir, "Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning," IEEE Robotics and Automation Letters, vol. PP, no. 99, pp. 1-8, https://doi.org/10.1109/lra.2026.3655207.
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IEEE Robotics and Automation Letters
Conference
Keywords
46 Information and Computing Sciences, 4602 Artificial Intelligence, 4611 Machine Learning
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Source
Publisher
Institute of Electrical and Electronics Engineers (IEEE)