Regressor-Based Model Adaptation for Shaping Deformable Linear Objects with Force Control

Many industrial and domestic applications involve the manipulation of deformable objects, such as wire, soft tissue, and food material. Compared to rigid objects, they are more challenging to manipulate. One reason being that it is difficult to obtain the exact deformation model, which relates to the external forces and the state of the object. This paper considers the problem of modeling and the force control of deformable linear objects (DLOs), where the force control input exerted on the DLO is related to the change of features along it. An online adaptation law is proposed to update the unknown model parameters, by exploring the property of linear parameterization; Then, a model-based force control scheme is applied to actively shape the DLO, in the absence of vision feedback. The proposed method has the advantages of data efficiency due to the prior physics model and of low computational complexity (and hence better real-time performance). The convergence of estimated parameters to the actual value is rigorously proved using Lyapunov methods. A series of comparative studies are presented to validate the efficient and effective performance of the proposed modeling method. Experimental results on model learning and shape control are also given to illustrate the proposed method.