  Hand guidance emerged from the safety requirements for collaborative robots,
namely possessing joint-torque sensors. Since then it has proven to be a
powerful tool for easy trajectory programming, allowing lay-users to reprogram
robots intuitively. Going beyond, a robot can learn tasks by user
demonstrations through kinesthetic teaching, enabling robots to generalise
tasks and further reducing the need for reprogramming. However, hand guidance
is still mostly relegated to collaborative robots. Here we propose a method
that doesn't require any sensors on the robot or in the robot cell, by using a
Microsoft HoloLens augmented reality head mounted display. We reference the
robot using a registration algorithm to match the robot model to the spatial
mesh. The in-built hand tracking and localisation capabilities are then used to
calculate the position of the hands relative to the robot. By decomposing the
hand movements into orthogonal rotations and propagating it down through the
kinematic chain, we achieve a generalised hand guidance without the need to
build a dynamic model of the robot itself. We tested our approach on a commonly
used industrial manipulator, the KUKA KR-5.
