In this paper, we propose a novel approach to automatically control the 3D shape of deformable wires using robots. Our approach proposes a novel visual feature along with a novel shape servoing method to enable dual arm manipulation of deformable wires. The visual feature relies on a geometric B-spline model and the use of Sequential Importance Resampling (SIR) particle filtering to track the 3D deformed shape of a wire over time. The shape servoing method is an adaptive modelfree method that iteratively updates the deformation Jacobian matrix using weighted least-squares minimization with sliding window and an eigenvalue-based confidence criterion. We performed several experiments on wires with different mechanical properties. The results show that our approach succeeded to control the 3D shape of various wires for many different desired deformations, while working at an interactive time. It has also been shown that the shape servoing method can be used to handle large deformations by subdividing the task in successive intermediary targets to reach. These promising results pave the way for automatic control of the 3D shapes of deformable wires in many fields such as catheter insertion in medicine or wire manipulation in industry.

In this paper, we propose the ADVISEd (Active Deformation through VIsual SErvoing) method, a novel modelfree deformation servoing method able to deform a soft object towards a desired shape. ADVISEd relies on an online estimation of the deformation Jacobian that relates the motion of the robot end-effector to the deformation behavior of the object. The estimation is based on a weighted least-squares minimization with a sliding window. The robustness of the method to observation noise is ensured using an eigenvaluebased confidence criterion. The ADVISEd method is validated through comparisons with a model-based and a model-free state-of-the-art methods. Two experimental setups are proposed to compare the methods, one to perform a marker-based active shaping task and one to perform several marker-less active shaping and shape preservation tasks. Experiments showed that our approach can interactively control the deformations of an object in different tasks while ensuring better robustness to external perturbations than the state-of-the-art methods.

In this paper, we propose a novel method to simultaneously track the deformation of soft objects and estimate their elasticity parameters. The tracking of the deformable object is performed by combining the visual information captured by a RGB-D sensor with interactive Finite Element Method simulations of the object. The visual information is more particularly used to distort the simulated object. In parallel, the elasticity parameter estimation minimizes the error between the tracked object and a simulated object deformed by the forces that are measured using a force sensor. Once the elasticity parameters are estimated, our tracking algorithm can be used to estimate the deformation forces applied to an object without the use of a force sensor. We validated our method on several soft objects with different shape complexities. Our evaluations show the ability of our method to estimate the elasticity parameters as well as its use to estimate the forces applied to a deformable object without any force sensor. These results open novel perspectives to better track and control deformable objects during robotic manipulations.