A predictive constraints selection model for periodic motion pattern generation

The paper proposes a periodic motion pattern generation model inspired by biological brain motor systems. It consists of three processing elements called, brain, CPG and body parts. The brain part is a time-series pattern discriminator modeled by RNN, which works as a predictive parameter selector for a subsequent CPG part. Moreover, the CPG part is a rhythmic pattern generator for a lower level motor control, which is represented by a neural oscillator model, and the body part corresponds to a physical dynamics between a controlled object and its environment. In the proposed schema, environmental perturbations can be stabilized by a mutual entrainment characteristic between the CPG and the body. In addition, the brain part can recognize several kinds of environmental changes via its proprioceptive feedback time-series stem from own actions, and it can predictively modulate the motion patterns by recalling well-suited CPG parameters according to the body dynamics.