  Telerobotic systems must adapt to new environmental conditions and deal with
high uncertainty caused by long-time delays. As one of the best alternatives to
human-level intelligence, Reinforcement Learning (RL) may offer a solution to
cope with these issues. This paper proposes to integrate RL with the Model
Mediated Teleoperation (MMT) concept. The teleoperator interacts with a
simulated virtual environment, which provides instant feedback. Whereas
feedback from the real environment is delayed, feedback from the model is
instantaneous, leading to high transparency. The MMT is realized in combination
with an intelligent system with two layers. The first layer utilizes Dynamic
Movement Primitives (DMP) which accounts for certain changes in the avatar
environment. And, the second layer addresses the problems caused by uncertainty
in the model using RL methods. Augmented reality was also provided to fuse the
avatar device and virtual environment models for the teleoperator. Implemented
on DLR's Exodex Adam hand-arm haptic exoskeleton, the results show RL methods
are able to find different solutions when changes are applied to the object
position after the demonstration. The results also show DMPs to be effective at
adapting to new conditions where there is no uncertainty involved.
