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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.