Before revision:  Existing literature demonstrates robot success in executing handovers with humans, albeit with substantial reliance on external sensing or with primitive signal processing methods, deficient compared to the rich set of information humans can detect. In contrast, we present models capable of distinguishing between four classes of human tactile gestures at a robot's end effector, using only a non-collocated six-axis force sensor at the wrist. Due to the absence in the literature, this work describes 1 ) the collection of an extensive force dataset characterized by human-robot contact events, and 2) classification models informed by this datasetto determine the nature of the interaction .
After revision:  Interpreting human touch gestures is a nuanced, challenging task with extreme gaps between human and robot capability. Among prior works that demonstrate human touch recognition capability, differences in sensors, gesture classes, feature sets, and classification algorithms yield a conglomerate of non-transferable results and a glaring lack of a standard. To address this gap, this work presents 1) four proposed touch gesture classes that cover the majority of the gesture characteristics identified in the literature, this work describes 1 ) the collection of an extensive force dataset characterized by human-robot contact events, and 2) classification models informed by this datasetto determine the nature of the interaction .