  This paper introduces a manipulation framework for the elastic rod, including
shape representation, sensorimotor-model estimation, and shape controller.
Until now, the manipulation of the elastic rod has faced several challenges: 1)
shape learning from high-dimensional to low-space dimensional; 2) the modeling
of robot manipulation of the elastic rod; 3) the determination of the shape
controller. A novel manipulation framework for the elastic rod is presented in
this paper, which only uses the input and output data of the system without any
prior knowledge of the robot, camera, and object. The proposed approach runs in
a model-free manner. For the approximation of the sensorimotor model, adaptive
Kalman filtering (AKF) is used as the online estimation. Model-free adaptive
control (MFAC) is designed according to the obtained differential model of
robot-object configuration and then is combined with the performance regulation
requirement to give the final format of the shape controller. Hence, the
proposed approach can enhance the autonomous capability of deformation object
manipulation. Detailed simulation results are conducted with a single robot
manipulation to evaluate the effectiveness of the proposed manipulation
framework.
