Simulations of imitative learning

Abstract

This Master thesis presents simulations within the field of imitative learning. The thesis starts with a review of the work done in my depth study, looking at imitative learning in general. Further, forward and inverse models are studied, and a case study of a Wolpert et al article is done. An architecture using the recurrent neural network with parametric bias (RNNPB) and a PID-controller by Tani et al is presented, and later simulated using MATLAB and the breve simulation environment. It is tested if the RNNPB is suitable for imitative learning. The first experiment was quite successful, and interesting results were discovered. The second experiment was less successful. Generally, it was confirmed that RNNPB is able to reproduce actions, interact with the environment, and indicate situations using the parametric bias (PB). It was also observed that the PB values tend to reflect common characteristics in similar training patterns. A comparison between the forward and inverse model and the RNNPB model was done. The former appears to be more modular and a predictor of consequence of actions, while the latter predicts sequences and is able to represent the situation it is in. The work done to connect MATLAB and breve is also presented.