Trajectory Learning based on Conditional Random Fields for Robot Programming by Demonstration

A. Vakanski, F. Janabi-Sharifi, I. Mantegh, A. Irish
2010 IASTED Technology Conferences / 705: ARP / 706: RA / 707: NANA / 728: CompBIO   unpublished
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
more » ... neralized trajectory for the task reproduction. The approach was evaluated by simulations of two industrial manufacturing applications.
doi:10.2316/p.2010.706-061 fatcat:63k2axqzbrg5xjsn6psj4ea32q