No abstract

No abstract

Object grasping is a typical human ability which is widely studied from both a biological and an engineering point of view. This paper presents an approach to grasp synthesis inspired by the human neurophysiology of actionoriented vision. Our grasp synthesis method is built upon an architecture which, taking into account the differences between robotic and biological systems, proposes an adaptation of brain models to the peculiarities of robotic setups. The architecture modularity allows for scalability and integration of complex robotic tasks. The grasp synthesis is designed as integrated with the extraction of a 3D object description, so that the object visual analysis is actively driven by the needs of the grasp synthesis: visual reconstruction is performed incrementally and selectively on the regions of the object that are considered more interesting for grasping.

No abstract

Abstract-In robotics, the manipulation of a priori unknown objects involves several steps and problems that must be carefully considered and solved by proper planning and control algorithms. For example, once suitable contact points have been computed, the control system should be able to track them in the approach phase, i.e., while the relative position/orientation of the object and the gripper of the robotic system change due to the approaching movement of the robot toward the object. This correspondence paper proposes a practical method for the tracking of grasp points in image space that is based on transferring previously computed grasp points from an initial image to subsequent ones and on the analysis of the new grasp configuration. Three different options are proposed for this transference. Experimental results show the interesting practical performance of the general procedure.