A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2013; you can also visit the original URL.
The file type is
IASTED Technology Conferences / 705: ARP / 706: RA / 707: NANA / 728: CompBIO
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 adoi:10.2316/p.2010.706-061 fatcat:63k2axqzbrg5xjsn6psj4ea32q