Title: Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand their Physical Limitations,
Abstract: Assistive robotics and particularly robot coaches may be very helpful for
rehabilitation healthcare. In this context, we propose a method based on
Gaussian Process Latent Variable Model (GP-LVM) to transfer knowledge between a
physiotherapist, a robot coach and a patient. Our model is able to map visual
human body features to robot data in order to facilitate the robot learning and
imitation. In addition , we propose to extend the model to adapt robots'
understanding to patient's physical limitations during the assessment of
rehabilitation exercises. Experimental evaluation demonstrates promising
results for both robot imitation and model adaptation according to the
patients' limitations.