Touch recognition and learning from demonstration (LfD) for collaborative human-robot firefighting teams

Abstract

In Navy human firefighting teams, touch is used extensively to communicate among teammates. In noisy, chaotic, and visually challenging environments, such as among fires on Navy ships, this is the only reliable means of communication. The overarching goal of this work is to augment Navy firefighting teams with an autonomous robot serving as a nozzle operator; to accomplish this, the robot must understand the tactile gestures of its human teammates. Preliminary results recognizing touch gestures have indicated the potential of such an autonomous system to serve as a nozzle operator in human-centric firefighting scenarios.