A Neural-Network based Approach for Recognition of Pose and Motion Gestures on a Mobile Robot

Since a variety of recent changes in both robotic hardware and software suggests that service robots will soon become possible, to nd \natural" ways of communication between human and robots is of fundamental importance for the robotic eld. This paper describes a gesture-based interface for human-robot interaction, which enables people to instruct robots through easy-to-perform arm gestures. Such gestures might be static pose gestures, which involve only a speci c con guration of the person's arm, or they might be dynamic motion gestures, that is, they involve motion (such as waving). Gestures are recognized in real-time at approximate frame rate, using neural networks. A fast, color-based tracking algorithm enables the robot to track and follow a person reliably through o ce environments with drastically changing lighting conditions. Results are reported in the context of an interactive clean-up task, where a person guides the robot to speci c locations that need to be cleaned, and the robot picks up trash which it then delivers to the nearest trash-bin.