  Integrating robots in complex everyday environments requires a multitude of
problems to be solved. One crucial feature among those is to equip robots with
a mechanism for teaching them a new task in an easy and natural way. When
teaching tasks that involve sequences of different skills, with varying order
and number of these skills, it is desirable to only demonstrate full task
executions instead of all individual skills. For this purpose, we propose a
novel approach that simultaneously learns to segment trajectories into
reoccurring patterns and the skills to reconstruct these patterns from
unlabelled demonstrations without further supervision. Moreover, the approach
learns a skill conditioning that can be used to understand possible sequences
of skills, a practical mechanism to be used in, for example,
human-robot-interactions for a more intelligent and adaptive robot behaviour.
The Bayesian and variational inference based approach is evaluated on synthetic
and real human demonstrations with varying complexities and dimensionality,
showing the successful learning of segmentations and skill libraries from
unlabelled data.
