• Publisher-DOI

Intelligent robotic assistants can potentially improve the quality of life for elderly people and help them maintain their independence. However, the number of different and personalized tasks render pre-programming of such assistive robots prohibitively difficult. Instead, to cope with a continuous and open-ended stream of cooperative tasks, new collaborative skills need to be continuously learned and updated from demonstrations. To this end, we introduce an online learning method for a skill library of collaborative tasks that employs an incremental mixture model of probabilistic interaction primitives. This model chooses a corresponding robot response to a human movement where the human intention is extracted from previously demonstrated movements. Unlike existing batch methods of movement primitives for human-robot interaction, our approach builds a library of skills online, in an open-ended fashion and updates existing skills using new demonstrations. The resulting approach was evaluated both on a simple benchmark task and in an assistive human-robot collaboration scenario with a 7DoF robot arm.