A probabilistic framework of learning movement primitives from unstructured demonstrations

Kinematic motor behaviour of robots can be encoded using dynamical systems. These dynamical systems are learnt from human demonstrations to generalize human like motions. The size of the demonstration space involving a robot usually being large, it is not possible to provide all the demonstrations for robot´s learning. Hence, it requires an efficient learning architecture that is able to generalize for unseen contexts. In the proposed algorithm, we model the movements, shown in the human demonstrations, as non-linear multivariate dynamics using mixture of Gaussians. Generally, in non-linear multivariate modelling approach pertaining to programming by demonstration, requires structured demonstrations i.e. it always requires to have a fixed and unique equilibrium point during the learning phase. The proposed method can relax these constraints and has the following advantage over the existing work: first, it would be possible to learn from any demonstration which is not constrained to have always the same equilibrium point; second, it would be possible to capture the variations in movement patterns depending upon the position of the equilibrium point. The proposed algorithm has been implemented using Barrett WAM and experimental results have been compared with existing approach.