  We propose a formalism for shared control, which is the problem of defining a
policy that blends user control and autonomous control. The challenge posed by
the shared autonomy system is to maintain user control authority while allowing
the robot to support the user. This can be done by enforcing constraints or
acting optimally when the intent is clear. Our proposed solution relies on
natural gradients emerging from the divergence constraint between the robot and
the shared policy. We approximate the Fisher information by sampling a learned
robot policy and computing the local gradient to augment the user control when
necessary. A user study performed on a manipulation task demonstrates that our
approach allows for more efficient task completion while keeping control
authority against a number of baseline methods.
