Lawson

Touch can be a powerful means of communication especially when it is combined with other sensing modalities, such as speech. The challenge on a humanoid robot is to sense touch in a way that can be sensitive to subtle cues, such as the hand used and amount of force applied. We propose a novel combination of sensing modalities to extract touch information. We extract hand information using the Leap Motion active sensor, then determine force information from force sensitive resistors. We combine these sensing modalities at the feature level, then train a support vector machine to recognize specific touch gestures. We demonstrate a high level of accuracy recognizing four different touch gestures from the firefighting domain.