Using Structured UKR Manifolds for Motion Classification and Segmentation

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Abstract

Task learning from observations of non-expert human users will be a core feature of future cognitive robots. However, the problem of task segmentation has only received minor attention. In this paper, we present a new approach to classifying and segmenting series of observations into a set of candidate motions. As basis for these candidates, we use structured UKR manifolds, a modified version of unsupervised kernel regression which has been introduced in order to easily reproduce and synthesise represented dextrous manipulation tasks. Together with the presented mechanism, it then realises a system that is able both to reproduce and recognise the represented motions.

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