Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems
Agrawal, Shreenabh Kussaba, Hugo TM Chen, Lingyun Binny, Allen Emmanuel Jagtap, Pushpak Haddadin, Sami Swikir, Abdalla
Agrawal, Shreenabh
Kussaba, Hugo TM
Chen, Lingyun
Binny, Allen Emmanuel
Jagtap, Pushpak
Haddadin, Sami
Swikir, Abdalla
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Robotics
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Conference proceeding
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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.
Citation
S. Agrawal, H.T.M. Kussaba, L. Chen, A.E. Binny, P. Jagtap, S. Haddadin, A. Swikir, "Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems," 2025, pp. 9917-9923.
Source
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Conference
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Keywords
46 Information and Computing Sciences, 4611 Machine Learning
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2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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IEEE