  In this paper, we present a novel learning-based shared control framework.
This framework deploys first-order Dynamical Systems (DS) as motion generators
providing the desired reference motion, and a Variable Stiffness Dynamical
Systems (VSDS) \cite{chen2021closed} for haptic guidance. We show how to shape
several features of our controller in order to achieve authority allocation,
local motion refinement, in addition to the inherent ability of the controller
to automatically synchronize with the human state during joint task execution.
We validate our approach in a teleoperated task scenario, where we also
showcase the ability of our framework to deal with situations that require
updating task knowledge due to possible changes in the task scenario, or
changes in the environment. Finally, we conduct a user study to compare the
performance of our VSDS controller for guidance generation to two
state-of-the-art controllers in a target reaching task. The result shows that
our VSDS controller has the highest successful rate of task execution among all
conditions. Besides, our VSDS controller helps reduce the execution time and
task load significantly, and was selected as the most favorable controller by
participants.
