Learning,Generating and Adapting Wave Gestures for Expressive Human-Robot Interaction
This addresses the problem of enabling expressive non-verbal communication in human-robot interaction, though it appears incremental as it builds on existing imitation learning methods.
The study tackled the problem of generating human-like rhythmic wave gestures for robots by proposing a novel imitation learning approach that modulates expression characteristics in the frequency domain, resulting in efficient encoding and modulation with ensured variability in simulated experiments on a 6-DOF robot.
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.