Learning, Generating and Adapting Wave Gestures for Expressive Human-Robot Interaction

Abstract

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.

Towards Culture-Aware Co-Speech Gestures for Social Robots

“…Finally, an imitation learning approach aimed at generating human-like rhytmic wave gestures has been recently proposed in [25]. The method approximates the gesture trajectory of the rythmic movement by solving the problem in the frequency domain, decomposing the signals as Fourier series.…”