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Telerobotic systems must adapt to new environmental conditions and deal withhigh uncertainty caused by long-time delays. As one of the best alternatives tohuman-level intelligence, Reinforcement Learning (RL) may offer a solution tocope with these issues. This paper proposes to integrate RL with the ModelMediated Teleoperation (MMT) concept. The teleoperator interacts with asimulated virtual environment, which provides instant feedback. Whereasfeedback from the real environment is delayed, feedback from the model isinstantaneous, leading to high transparency. The MMT is realized in combinationwith an intelligent system with two layers. The first layer utilizes DynamicMovement Primitives (DMP) which accounts for certain changes in the avatarenvironment. And, the second layer addresses the problems caused by uncertaintyin the model using RL methods. Augmented reality was also provided to fuse theavatar device and virtual environment models for the teleoperator. Implementedon DLR's Exodex Adam hand-arm haptic exoskeleton, the results show RL methodsare able to find different solutions when changes are applied to the objectposition after the demonstration. The results also show DMPs to be effective atadapting to new conditions where there is no uncertainty involved.

Model Mediated Teleoperation with a Hand-Arm Exoskeleton in Long Time Delays Using Reinforcement Learning