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Human teams are able to easily perform collaborative manipulation tasks.However, for a robot and human to simultaneously manipulate an extended objectis a difficult task using existing methods from the literature. Our approach inthis paper is to use data from human-human dyad experiments to determine motionintent which we use for a physical human-robot co-manipulation task. We firstpresent and analyze data from human-human dyads performing co-manipulationtasks. We show that our human-human dyad data has interesting trends includingthat interaction forces are non-negligible compared to the force required toaccelerate an object and that the beginning of a lateral movement ischaracterized by distinct torque triggers from the leader of the dyad. We alsoexamine different metrics to quantify performance of different dyads. We alsodevelop a deep neural network based on motion data from human-human trials topredict human intent based on past motion. We then show how force and motiondata can be used as a basis for robot control in a human-robot dyad. Finally,we compare the performance of two controllers for human-robot co-manipulationto human-human dyad performance.

Human-robot co-manipulation of extended objects: Data-driven models and control from analysis of human-human dyads