  This study proposes a novel imitation learning approach for the stochastic
generation of human-like rhythmic wave gestures and their modulation for
effective non-verbal communication through a probabilistic formulation using
joint angle data from human demonstrations. This is achieved by learning and
modulating the overall expression characteristics of the gesture (e.g., arm
posture, waving frequency and amplitude) in the frequency domain. The method
was evaluated on simulated robot experiments involving a robot with a
manipulator of 6 degrees of freedom. The results show that the method provides
efficient encoding and modulation of rhythmic movements and ensures variability
in their execution.
