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- W4383172021 abstract "This paper proposes a novel autonomous dynamic system (ADS) based controller for trajectory learning from demonstration (LfD). We call our method Learning Stable Dynamics via Iterative Quadratic Programming (LSD-IQP). LSD-IQP learns an energy function and an ADS from demonstrations via semi-infinite quadratic programming. Energy function constraints are imposed on the learned ADS to ensure convergence to a single goal position. Unlike other energy-based methods, LSD-IQP allows the energy function to have both local maximums and saddle points. This flexibility enables LSD-IQP to learn a broader class of motions compared to other ADS-based controllers. We demonstrate the capabilities of LSD-IQP via several experiments, including: 1) learning handwritten symbols and comparing the swept error area to several other ADS methods 2) learning a pick-and-place task with novel goal positions for a robot, and 3) learning a point to point motion in the presence of a non-convex obstacle for a robot." @default.
- W4383172021 created "2023-07-05" @default.
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- W4383172021 date "2023-05-29" @default.
- W4383172021 modified "2023-10-15" @default.
- W4383172021 title "Learning Stable Dynamics via Iterative Quadratic Programming" @default.
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- W4383172021 doi "https://doi.org/10.1109/icra48891.2023.10161237" @default.
- W4383172021 hasPublicationYear "2023" @default.
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