Multilateral teleoperated robots can be used to train humans to perform complex tasks that require collaborative interaction and expert supervision, such as laparoscopic surgical procedures. In this paper, we explain the design and performance evaluation of a shared-control architecture that can be used in trilateral teleoperated training robots. The architecture includes dominance and observation factors inspired by the determinants of motor learning in humans, including observational practice, focus of attention, feedback and augmented feedback, and self-controlled practice. Toward the validation of such an architecture, we (1) verify the stability of a trilateral system by applying Llewellyn's criterion on a two-port equivalent architecture, and (2) demonstrate that system transparency remains generally invariant across relevant observation factors and movement frequencies. In a preliminary experimental study, a dyad of two human users (one novice, one expert) collaborated on the control of a robot to follow a trajectory. The experiment showed that the framework can be used to modulate the efforts of the users and adjust the source and level of haptic feedback to the novice user.
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View details for PubMedID 26737388