Using on-line Conditional Random Fields to determine human intent for peer-to-peer human robot teaming

Abstract

In this paper we introduce a system under development to enable humans and robots to collaborate as peers on tasks in a shared physical environment, using only implicit coordination. Our system uses Conditional Random Fields to determine the human's intended goal. We show the effects of using different features to improve accuracy and the time to the correct classification. We compare the performance of the Conditional Random Fields classifiers by testing the classification accuracy with both the full observation sequence, as well as accuracy when the observations are classified as the observations occur. We show that Conditional Random Fields work well for classifying the goal of a human in a box pushing domain where the human can select one of three tasks. We discuss how this research fits into a larger system we are developing for peer-to-peer human robot teams for shared workspace interactions.