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This paper introduces a manipulation framework for the elastic rod, includingshape 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 modelingof robot manipulation of the elastic rod; 3) the determination of the shapecontroller. A novel manipulation framework for the elastic rod is presented inthis paper, which only uses the input and output data of the system without anyprior knowledge of the robot, camera, and object. The proposed approach runs ina model-free manner. For the approximation of the sensorimotor model, adaptiveKalman filtering (AKF) is used as the online estimation. Model-free adaptivecontrol (MFAC) is designed according to the obtained differential model ofrobot-object configuration and then is combined with the performance regulationrequirement to give the final format of the shape controller. Hence, theproposed approach can enhance the autonomous capability of deformation objectmanipulation. Detailed simulation results are conducted with a single robotmanipulation to evaluate the effectiveness of the proposed manipulationframework.

Adaptive Shape-Servoing for Vision-based Robotic Manipulation with Model Estimation and Performance Regulation