Gesture Recognition for Initiating Human-to-Robot Handovers

Abstract

Human-to-Robot handovers are useful for many Human-Robot Interaction scenarios. It is important to recognize when a human intends to initiate handovers, so that the robot does not try to take objects from humans when a handover is not intended. We pose the handover gesture recognition as a binary classification problem in a single RGB image. Three separate neural network modules for detecting the object, human body key points and head orientation, are implemented to extract relevant features from the RGB images, and then the feature vectors are passed into a deep neural net to perform binary classification. Our results show that the handover gestures are correctly identified with an accuracy of over 90%. The abstraction of the features makes our approach modular and generalizable to different objects and human body types.

Body Gesture Recognition to Control a Social Mobile Robot

“…Other works focus on identifying emotions expressed by the body posture, such as [11]. Moreover, Some gestures are very restricted to specific tasks, in [7] the approach is to detect whether a human is willing to collaborate or not during a hand-over operation according to his gestural expression.…”