On the Quality-of-Learning for Haptic Teleoperation-based Skill Transfer Over the Tactile Internet

Transfer of skills and teaching tasks to robots face new challenges when the demonstrations are provided remotely via tele-operation. Not only having a remote operator, but also the communication between the tele-operator and the operator affects the quality of demonstrations. Artifacts introduced by lossy haptic data compression and communication delay deteriorate the system transparency; however, the impact of these on the quality of learning has not been studied yet. In this paper, we construct the bridge between the learning quality and the reduced transparency caused by lossy haptic data compression during teleoperation with haptic feedback. The considered haptic data compression scheme is the previously proposed perceptual dead band-based kinesthetic data reduction approach. The learning quality is assessed both with the mean squared error (MSE) metric on the trajectory level and with the rate of success defined on the task requirement. Our experiments show that the learning quality is reduced significantly for a dead band parameter larger than 20% and 30% for a cube following and peg-in-hole tasks, respectively.