We propose an online iterative path optimisation
method to enable a Baxter humanoid robot to assist human
users to dress. The robot searches for the optimal personalised
dressing path using vision and force sensor information: vision
information is used to recognise the human pose and model the
movement space of upper-body joints; force sensor information
is used for the robot to detect external force resistance and
to locally adjust its motion. We propose a new stochastic path
optimisation method based on adaptive moment estimation. We
first compare the proposed method with other path optimisation
algorithms on synthetic data. Experimental results show that
the performance of the method achieves the smallest error with
fewer iterations and less computation time. We also evaluate
real-world data by enabling the Baxter robot to assist real
human users with their dressing.