In recent years, there has been a growing interest in robotic manipulation of deformable objects. In order to perform certain tasks, the robot must control the shape of the object while taking care not to apply excessive stresses so as not to deform it irreversibly. This is the case when extracting elasto-plastic objects in strips from an industrial reel. In order to control the mechanical stresses within the object, we propose a vision-based control scheme to minimize tension by regulating the angular velocity of a motorized reel on which they are wound. In this paper, we propose a method, based on a catenary model and visual feedback from a low-cost RGB-D camera, to estimate the tension distribution along a rubber strip. Thus, the control strategy aims to achieve a desired tension value by varying the length of the suspended portion of the manipulated strip. Simulation and experimental results validate the proposed approach for strip-like objects of various dimensions.

Most deformable object manipulation tasks still rely on skillful human operators. To automate such tasks, a robotic system should not only be able to deform an object to a desired shape but also servo its deformation along a specific path towards the desired shape. We propose a shape servoing control scheme to automate such tasks. Our scheme controls the deformation trajectory towards the desired shape by imposing task-focused convergence constraints. The constraints impose how fast the different regions of the object converge to the desired shape. Integrating such a behavior in shape servoing forms our main contribution. Experiments, carried out on rubber layer assembly tasks, show that our control scheme outperforms a state-of-the-art shape servoing scheme.

Gestures classification is a widespread solution for prosthetic hand control using electromyographic signals (EMG). It can be defined as the task of estimating the class associated to a time-series data provided by the sensor. Nevertheless, it remains unsatisfactory as the number of motions (classes) is limited. In this article, we investigate the problem of estimating the joint coordinates of the hand from the same input data. The main contributions of the article are: (1) a new dataset for joint coordinates estimation from EMG data, (2) a set of neural networks models for this regression task, and (3) an experimental study to compare the performances of the proposed methods. The database is built with a six channels EMG armband and hand motions measured by computer vision. First results are hopeful and provide both a baseline for new methods and a proof of concept for future prosthetic control solutions.

Robots are nowadays faced with the challenge of handling deformable objects in industrial operations. In particular, the problem of shape control, which aims at giving a specific deformation state to an object, has gained interest recently in the research community. Among the proposed solutions, approaches based on finite elements proved accurate and reliable but also complex and computationally-intensiveIn order to mitigate these drawbacks, we propose a scheme for shape control that does not require to run a real-time simulation or to solve an implicit optimization problem for computing the control outputs. It is based on a partition of the nodal coordinates that allows deriving a control law directly from tangent stiffness matrices. This formulation is also coupled with the introduction of reduced finite element models. Simulation and experimental results in the context of linear deformable object manipulation demonstrate the interest of the proposed approach.

The 3-CRS manipulator is an original parallel mechanism having 6 degrees of freedom (DOFs) with only 3 limbs. This mechanism uses a motorized cylindrical joint per limb. This new paradigm of actuation opens research fields on new families of robots that should particularly interest the parallel robotics community. According to its dimensional synthesis, this mechanism can have remarkable kinematic properties such as a large orientation workspace or reconfiguration capabilities. In this paper, we introduce this mechanism and we study its singularities by using a geometric approach. This approach simplifies considerably singularity analysis problem by considering the relative geometric configurations of three planes defined by the distal links of the limbs. Thanks to that, a reconfiguration mode of the 3-CRS, that doubles its reachable workspace, is highlighted. This property is illustrated on a physical prototype of the robot.

Precise robot manipulation of deformable objects requires an accurate and fast estimation of their shape as they deform. So far, visual sensing has been mostly used to solve this issue, but vision sensors are sensitive to occlusions, which might be inevitable when manipulating an object with robot. To address this issue, we present a modular pipeline to track the shape of a soft object in an online manner by coupling tactile sensing with a deformation model. Using a model of a tactile sensor, we compute the magnitude and location of a contact force and apply it as an external force to the deformation model. The deformation model then updates the nodal positions of a mesh that describes the shape of the deformable object. The proposed sensor model and pipeline, are evaluated using a Shadow Dexterous Hand equipped with BioTac sensors on its fingertips and an RGB-D sensor.