  Many industries extensively use flexible materials. Effective approaches for
handling flexible objects with a robot manipulator must address residual
vibrations. Existing solutions rely on complex models, use additional
instrumentation for sensing the vibrations, or do not exploit the repetitive
nature of most industrial tasks. This paper develops an iterative learning
control approach that jointly learns model parameters and residual dynamics
using only the interoceptive sensors of the robot. The learned model is
subsequently utilized to design optimal (PTP) trajectories that accounts for
residual vibration, nonlinear kinematics of the manipulator and joint limits.
We experimentally show that the proposed approach reduces the residual
vibrations by an order of magnitude compared with optimal vibration suppression
using the analytical model and threefold compared with the available
state-of-the-art method. These results demonstrate that effective handling of a
flexible object does not require neither complex models nor additional
instrumentation.
