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- W2334382613 abstract "This work presents an approach for implementation of conditional random fields (CRF) in transferring motor skills to robots. As a discriminative probabilistic model, CRF models directly the conditional probability distribution over label sequences for given observation sequences. Hereby, CRF was employed for segmentation and labeling of a set of demonstrated trajectories observed by a tracking sensor. The key points obtained by CRF segmentation of the demonstrations were used for generating a generalized trajectory for the task reproduction. The approach was evaluated by simulations of two industrial manufacturing applications." @default.
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- W2334382613 date "2010-01-01" @default.
- W2334382613 modified "2023-10-01" @default.
- W2334382613 title "Trajectory Learning based on Conditional Random Fields for Robot Programming by Demonstration" @default.
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- W2334382613 doi "https://doi.org/10.2316/p.2010.706-061" @default.
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