Supervised Learning of Gesture-Action Associations for Human-Robot Collaboration

As human-robot collaboration methodologies develop robots need to adapt fast learning methods in domestic scenarios. The paper presents a novel approach to learn associations between the human hand gestures and the robot's manipulation actions. The role of the robot is to operate as an assistant to the user. In this context we propose a supervised learning framework to explore the gesture-action space for human-robot collaboration scenario. The framework enables the robot to learn the gesture-action associations on the fly while performing the task with the user; an example of zero-shot learning. We discuss the effect of an accurate gesture detection in performing the task. The accuracy of the gesture detection system directly accounts for the amount of effort put by the user and the number of actions performed by the robot.