Knowledge Transfer for Learning Robot Models via Local Procrustes Analysis

Ndivhuwo Makondo, Benjamin Rosman, Osamu Hasegawa

Abstract:

Learning of robot kinematic and dynamic models
from data has attracted much interest recently as an alternative
to manually defined models. However, the amount of data
required to learn these models becomes large when the number
of degrees of freedom increases and collecting it can be a timeintensive
process. We employ transfer learning techniques in
order to speed up learning of robot models, by using additional
data obtained from other robots. We propose a method for
approximating non-linear mappings between manifolds, which
we call Local Procrustes Analysis (LPA), by adopting and
extending the linear Procrustes Analysis method. Experimental
results indicate that the proposed method offers an accurate
transfer of data and significantly improves learning of the
forward kinematics model. Furthermore, it allows learning
a global mapping between two robots that can be used to
successfully transfer trajectories.