Active Deformation through Visual Servoing of Soft Objects

摘要

In this paper, we propose the ADVISEd (Active Deformation through VIsual SErvoing) method, a novel model-free 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 eigenvalue-based 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.