Shape Control of Elastic Objects Based on Implicit Sensorimotor Models and Data-Driven Geometric Features
This work addresses the problem of automatic shape control for elastic objects, which is relevant for robotics and manufacturing applications, representing an incremental step in this domain.
This paper proposes an automatic control method for manipulating elastic objects into desired shapes, using real-time visual feedback and data-driven geometric models. The method analytically computes a pose-shape Jacobian matrix based on implicit functions to derive a shape servoing controller, validated through experiments with a robotic manipulator deforming an elastic rod.
This paper proposes a general approach to design automatic controls to manipulate elastic objects into desired shapes. The object's geometric model is defined as the shape feature based on the specific task to globally describe the deformation. Raw visual feedback data is processed using classic regression methods to identify parameters of data-driven geometric models in real-time. Our proposed method is able to analytically compute a pose-shape Jacobian matrix based on implicit functions. This model is then used to derive a shape servoing controller. To validate the proposed method, we report a detailed experimental study with robotic manipulators deforming an elastic rod.