Managing Off-Nominal Events in Shared Teleoperation with Learned Task Compliance

Abstract

This article studies imitation learning policies that encode task compliance to provide teleoperation assistance for remote manufacturing. The central challenge is how to handle off-nominal situations, such as out-of-sequence work or unplanned obstacles, since the assistance has not been trained to handle such scenarios. In such cases, there is potential for the assistance to degrade-rather than improve-operator performance. This work proposes a method that exploits the learned task compliance to classify persistent human actions as off-nominal, and attenuate assistance in these regions. Applied to a hole-cleaning task with n = 11 subjects, the proposed method shows up to 17% reduction in task completion time and up to 68% reduction in forces in off-nominal situations as compared to assistance without the method. Additionally, the method retains the performance improvements of assistance in nominal operating regimes.