Learning human motion primitives with multimodal non-negative matrix factorization
Channel: Inria Flowers
Date: 2012-10-24

Learning combinatorial human motion primitives with multimodal non-negative matrix factorization

Keywords: imitation learning, robotics, human behavior understanding, motion primitive, dictionary learning, multimodal learning, dance

We present an approach, based on non-negative matrix factorization, for learning to recognize parallel combinations of initially unknown human motion primitives, associated with ambiguous sets of linguistic labels during training. In the training phase, the learner observes a human producing complex motions which are parallel combinations of initially unknown motion primitives. Each time the human shows a complex motion, he also provides high-level linguistic descriptions, consisting of a set of labels giving the name of the primitives inside the complex motion. From the observation of multi-modal combinations of high-level labels with high-dimensional continuous unsegmented values representing complex motions, the learner must later on be able to recognize, through the production of the adequate set of labels, which are the motion primitives in a novel complex motion produced by a human, even if those combinations were never observed during training. We explain how this problem, as well as natural extensions, can be addressed using non-negative matrix factorization. Then, we show in an experiment in which a learner has to recognize the primitive motions of complex human dance choreographies, that this technique allows the system to infer with good performance the combinatorial structure of parallel combinations of unknown primitives.

More details can be found in the paper :
Mangin O., Oudeyer P.Y., Learning to recognize parallel combinations of human motion primitives with linguistic descriptions using non-negative matrix factorization. IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Algarve (Portugal), 2012. 
http://olivier.mangin.com/publi.html#Mangin.2012.IROS
https://flowers.inria.fr/ManginOudeyerIROS12.pdf