A flat neural network architecture to represent movement primitives with integrated sequencing

The paper proposes a minimalistic network to learn a set of movement primitives and their sequencing in one single feedforward network. Utilizing an extreme learning machine with output feedback and a simple inhibition mechanism, this approach can sequence movement primitives efficiently with very moderate network size. It can interpolate movement primitives to create new motions. This work thus demonstrates that an unspecific single hidden layer, that is a flat representation is sufficient to efficiently compose complex sequences, a task which usually requires hierarchy, multiple timescales and multi-level control mechanisms.