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

A. Vakanski, F. Janabi-Sharifi, I. Mantegh, and A. Irish (Canada)


Robotics, Programming by Demonstration, Industrial Automation, Artificial Intelligence.


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.

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