Real-time Feasibility of a Human Intention Method Evaluated Through a Competitive Human-Robot Reaching Game

Abstract

Predicting human behavior is a necessary robot ability for safe and fluent human-robot collaboration in shared workspaces. Robots should recognize human intended actions by combining information from ongoing movements and other environmental cues. In many cases, visual sensors might be required for obtaining information regarding the human move-ment. While using cameras, especially a single one, offers several benefits, the information captured is noisy and of relative low frequency considering the requirements for intention prediction. The purpose of this study was to evaluate the feasibility of obtaining real-time intention prediction and using it timely for robot action, when human behavior is observed by a single RGB-D camera. Visual information is used to obtain human joint data using Openpose. Based on this we then construct appropriate features and train several Machine Learning models. We evaluate the feasibility of timely robot action using a competitive human-robot game. The results show that a prediction available at about 288ms is early enough to enable timely robot action provided that the robot has to act on objects that are no farther than 10 cm away.