Learning to gesticulate by observation using a deep generative approach
This addresses the problem of making humanoid robots more expressive and engaging during conversations, though it appears incremental as it applies existing GAN methods to a specific robotics task.
The paper tackled generating natural talking gestures for a humanoid robot by training a Generative Adversarial Network (GAN) on human gesture data from a Kinect, resulting in the robot producing a wide variety of non-dreary, natural gestures as shown in videos.
The goal of the system presented in this paper is to develop a natural talking gesture generation behavior for a humanoid robot, by feeding a Generative Adversarial Network (GAN) with human talking gestures recorded by a Kinect. A direct kinematic approach is used to translate from human poses to robot joint positions. The provided videos show that the robot is able to use a wide variety of gestures, offering a non-dreary, natural expression level.