Robust learning of arm trajectories through human demonstration

We present a model, composed of a hierarchy of artificial neural networks, for robot learning by demonstration. The model is implemented in a dynamic simulation of a 41 degrees of freedom humanoid for reproducing 3D human motion of the arm. Results show that the model requires little information about the desired trajectory and learns on-line the relevant features of movement. It can generalize across a small set of data to produce a qualitatively good reproduction of the demonstrated trajectory. Finally, it is shown that reproduction of the trajectory after learning is robust against perturbations