Classification of handover interaction primitives in a COBOT-human context with a deep neural network

Object handover between humans and robots is an extremely important concern and lays on the edge of human-robot interaction challenges. This paper proposes a new approach to classify physical human interactions with a COBOT for handover operations. The human actions are categorized in simple interaction primitives. A deep neural network was devised and trained to classify torques and forces measured on the robot into one of four interaction primitives, namely pull, push, shake and twist. More specifically, an input vector of forces and torques observed and accumulated during a short span of half a second is fed into a properly trained feedforward deep neural network that classifies the interaction as one of the four primitives established. A specific dataset was created using several persons that provided abundant data both for training and testing. The results are very good not only in the training phase demonstrated by the convergence indicators, but also in the testing phase where interactions from previously unseen operators were successfully classified with an outstanding confidence.