Human-human haptic collaboration in cyclical Fitts´ tasks

Understanding how humans assist each other in haptic interaction teams could lead to improved robotic aids to solo human dextrous manipulation. Inspired by experiments reported in Reed et al. (2004), which suggested two-person haptically interacting teams could achieve a lower movement time (MT) than individuals for discrete aiming movements of specified accuracy, we report that two-person teams (dyads) can also achieve lower MT for cyclical, continuous aiming movements. We propose a model, called endpoint compromise, for how the intended endpoints of both subjects´ motion combine during haptic interaction; it predicts a ratio of √2 between slopes of MT fits for individuals and dyads. This slope ratio prediction is supported by our data.