Load estimation and control using learned dynamics models
Giorgos Petkos and Sethu Vijayakumar
In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '07), 29 Oct - 2 Nov, 2007, San Diego, USA.
Classic adaptive control methods for handling
varying loads rely on an analytically derived model of the
robot’s dynamics. However, in many situations, it is not feasible
or easy to obtain an accurate analytic model of the robot’s
dynamics. An alternative to analytically deriving the dynamics
is learning the dynamics from movement data. This paper
describes a load estimation technique that uses the learned
instead of analytically derived dynamics. We study examples
where the various inertial parameters of the load are estimated
from the learned models, their effectiveness in control is
evaluated along with their robustness in light of imperfect,
intermediate dynamic models.