Using Bayesian Dynamical Systems for Motion Template Libraries

Motor primitives or motion templates have become an important concept for both modeling human motor control as well as generating robot behaviors using imitation learning. Recent impressive results range from humanoid robot movement generation to timing models of human motions. The automatic generation of skill libraries containing multiple motion templates is an important step in robot learning. Such a skill learning system needs to cluster similar movements together and represent each resulting motion template as a generative model which is subsequently used for the execution of the behavior by a robot system. In this paper, we show how human trajectories captured as multi-dimensional time-series can be clustered using Bayesian mixtures of linear Gaussian state-space models based on the similarity of their dynamics. The appropriate number of templates is automatically determined by enforcing a parsimonious parametrization. As the resulting model is intractable, we introduce a novel ...

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Silvia Chiappa, Jens Kober, Jan Peters
• Keywords: Generative Model, Information Technology, Motion Templates, NIPS 2008, Robot