A neural network based approach for recognition of pose and motion gestures on a mobile robot

Abstract

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 inreal-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. 1