  Steerable needles are minimally invasive devices that enable novel medical
procedures by following curved paths to avoid critical anatomical obstacles.
Planning algorithms can be used to find a steerable needle motion plan to a
target. Deployment typically consists of a physician manually inserting the
steerable needle into tissue at the motion plan's start pose and handing off
control to a robot, which then autonomously steers it to the target along the
plan. The handoff between human and robot is critical for procedure success, as
even small deviations from the start pose change the steerable needle's
workspace and there is no guarantee that the target will still be reachable. We
introduce a metric that evaluates the robustness to such start pose deviations.
When measuring this robustness to deviations, we consider the tradeoff between
being robust to changes in position versus changes in orientation. We evaluate
our metric through simulation in an abstract, a liver, and a lung planning
scenario. Our evaluation shows that our metric can be combined with different
motion planners and that it efficiently determines large, safe start regions.
