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Learning from Demonstration is an important technology for the new wave of robots that are envisioned to work side-by-side with workers in factories as well as social robots. Most available techniques for learning from demonstration rely on the existence of a training set of demonstrations that is assumed to be pre-segmented and is usually processed in batch. Another problem with most available methods is the need to set the model complexity used to model the motion. In this paper, we propose a solution to both problems based on incremental piecewise linear segmentation of the motion using an extension of the SWAB algorithm. Evaluation experiments show that the proposed method is able to generate motion models with adequate complexity without the need for model comparison methods and assuming incremental streaming of demonstrations rather than the availability of the complete training set in advance. The proposed method is applicable not only to robot learning from demonstration but to other industrial applications in which an accurate model of time variant data needs to be built from streaming input.

Simple Incremental GMM Modeling using Multidimensional Piecewise Linear Segmentation for Learning from Demonstration