Visual tracking of a jaw gripper based on articulated 3D models for grasping

Robust grasping of objects in uncertainty conditions can be achieved with the visual monitoring of the interaction between the robot hand and the object. In this paper we propose a new approach for the visual tracking of a robot hand suitable to observe the interaction between robot and object. It consists on the continuous vision-based recovery of the articular pose of the robot hand. It is based on the principles of virtual visual servoing, which allows to deal with articulated bodies and occlusions. Its suitability is shown by tracking a parallel jaw gripper under different conditions such as self-occlusion, articulated motion and important changes in the point-of-view. The potential applications range from estimating the robot hand articulated pose under poor hand-eye calibration and joint feedback, until detecting deficient contact configurations, incipient slips, etc.