A Novel Haptic Feature Set for the Classification of Interactive Motor Behaviors in Collaborative Object Transfer.

Abstract

Haptics provides a natural and intuitive channel of communication during the interaction of two humans in complex physical tasks, such as joint object transportation. However, despite the utmost importance of touch in physical interactions, the use of haptics is under-represented when developing intelligent systems. This ar̥ticle explores the prominence of haptic data to extract information about underlying interaction patterns within physical human-human interaction (pHHI). We work on a joint object transportation scenario involving two human partners, and show that haptic features, based on force/torque information, suffice to identify human interactive behavior patterns. We categorize the interaction into four discrete behavior classes. These classes describe whether the partners work in harmony or face conflicts while jointly transporting an object through translational or rotational movements. In an experimental study, we collect data from $\mathbf {12}$ human dyads and verify the salience of haptic features by achieving a correct classification rate over $\mathbf {91\%}$ using a Random Forest classifier.