Extending extrapolation capabilities of probabilistic motion models learned from human demonstrations using shape-preserving virtual demonstrations

Learning from Demonstration (LfD) requires methodologies able to generalize tasks in new situations. This paper studies the use of virtual demonstrations to extend the extrapolation capabilities of probabilistic motion models such as the traPPCA method. Similarly to other LfD methods, traPPCA is able to calculate new trajectories very fast, but does not generalize well outside the area covered by the demonstrations. Another approach, the invariants method, shows outstanding generalization capabilities thanks to its shape-preserving prop-erties, while being limited by long computation times. The pro-posed methodology combines the advantages of the two methods by learning traPPCA models using virtual demonstrations generated by the invariants method. The proposed approach is analyzed in three case studies. Furthermore, a comparison is made between learning with virtual demonstrations and learning with only real demonstrations. The results encourage the use of virtual demonstrations to extend the extrapolation capabilities of probabilistic motion models and hence reduce the required number of real demonstrations. The latter has the potential of reducing the cost of commissioning robot tasks.