  In recent years, there has been a booming shift in the development of
versatile, autonomous robots by introducing means to intuitively teach robots
task-oriented behaviour by demonstration. In this paper, a method based on
programming by demonstration is proposed to learn null space policies from
constrained motion data. The main advantage to using this is generalisation of
a task by retargeting a systems redundancy as well as the capability to fully
replace an entire system with another of varying link number and lengths while
still accurately repeating a task subject to the same constraints. The
effectiveness of the method has been demonstrated in a 3-link simulation and a
real world experiment using a human subject as the demonstrator and is verified
through task reproduction on a 7DoF physical robot. In simulation, the method
works accurately with even as little as five data points producing errors less
than 10^-14. The approach is shown to outperform the current state-of-the-art
approach in a simulated 3DoF robot manipulator control problem where motions
are reproduced using learnt constraints. Retargeting of a systems null space
component is also demonstrated in a task where controlling how redundancy is
resolved allows for obstacle avoidance. Finally, the approach is verified in a
real world experiment using demonstrations from a human subject where the
learnt task space trajectory is transferred onto a 7DoF physical robot of a
different embodiment.
