  Shared control in teleoperation leverages both human and robot's strengths
and has demonstrated great advantages of reducing the difficulties in
teleoperating a robot and increasing the task performance. One fundamental
question in shared control is how to effectively allocate the control power to
the human and robot. Researchers have been subjectively defining the arbitrate
policies following conflicting principles, which resulted in great
inconsistency in the policies. We attribute this inconsistency to the
inconsiderateness of the multi-resource uncertainty in the human-robot system.
To fill the gap, we developed a multi-source uncertainty model that was
applicable to various types of uncertainty in real world, and then a general
arbitration model was developed to comprehensively fuse the uncertainty and
regulate the arbitration weight assigned to the robotic agent. Beside
traditional macro performance metrics, we introduced objective and quantitative
metrics of robotic helpfulness and friendliness that evaluated the assistive
robot's cooperation at micro and macro levels. Results from simulations and
experiments showed the new arbitration model was more effective and friendly
over the existing policies and was robust to coping with multi-source
uncertainty. With this new arbitration model, we expect the increased adoption
of human-robot shared control in practical and complex teleoperation tasks.
