Performance Evaluation of Automatic Gesture Generation System Using Bi-Directional LSTM on Humanoid Robot

Abstract

Conventional lecture substitution systems with humanoid robots use pre-defined gestures created by hand. Automatically generating these gestures makes it possible to create gestures without requiring expert knowledge and work, which is expected to lead to further progress in research on lecture substitution systems. This paper proposes an automatic gesture generation method which is expected to consider the semantic context of an utterance. Our proposed method is implemented by using a deep neural network with Bi-Directional LSTM units, applying filters for data correction, and axis conversion.