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.

翻译：本文提出了一种针对弹性杆的操作框架，涵盖形状表示、感知运动模型估计及形状控制器。目前弹性杆操作面临若干挑战：1）从高维到低维空间的形状学习；2）机器人操作弹性杆的建模；3）形状控制器的确定。本文提出了一种新颖的弹性杆操作框架，该框架仅利用系统的输入输出数据，无需任何关于机器人、相机及物体的先验知识，并以无模型方式运行。为逼近感知运动模型，采用自适应卡尔曼滤波作为在线估计方法。根据得到的机器人与物体配置的差分模型，设计无模型自适应控制，并结合性能调节需求形成形状控制器的最终形式。因此，所提方法可增强形变物体操作的自主能力。通过单机器人操作的详细仿真结果验证了所提操作框架的有效性。