This paper deals with enhancing the level of autonomy in a robotic work cell. With that mission in mind, the authors present an integrated framework for the sensing, the planning, and the execution aspects of assembly. In experimental demonstrations of this system on a PUMA762, the authors can now throw objects randomly into the workspace of the robot and the robot then automatically synthesizes a manipulation plan that includes the operations of sensing, grasping, and regrasping. Each operation is invoked only when it is deemed necessary for the successful execution of assembly.

This article presents a new theorem and an algorithm for fast synthesis of two-fingered force-closure grasps for arbitrary polygonal objects. The polygonal objects are allowed to be of arbitrary shape, in the sense that there is no restriction that the objects be convex. Moreover, each edge of the polygon is allowed to have different frictional characteristics. Our novel formulation results in a simple and efficient algorithm that finds grasps that tolerate the largest positioning errors for each of the fingertips at the contact points. The algorithm is both complete and correct, meaning that if any force-closure grasp exists for the object, the algorithm will find it; furthermore, all the grasps synthesized by the algorithm are guaranteed to be valid force-closure grasps.

This paper deals with the automatic programming of robots. We present here a method in which a robot can learn new assembly tasks by monitoring the performance of a human operator wearing a DataGlove. In particular, the system records the motions of the DataGlove as it is used to manipulate actual objects and, by using geometric reasoning, deduces the assembly task that is being performed. Subsequently, the assembly steps thus deduced are translated automatically by a task planner into a robot manipulation program. In experimental demonstrations of this system, the robot can now learn assembly tasks involving pickups, putdowns and various mating operations.

This paper deals with enhancing the level of autonomy in a robotic work cell. With that mission in mind, we present here an integrated framework for the sensing, the planning, and the execution aspects of assembly. In experimental demonstrations of this system on a PUMA 762, we can now throw objects randomly into the work space of the robot and the robot then automatically synthesizes a manipulation plan that includes the operations of sensing and regrasping only when these are deemed necessary for the successful execution of assembly.>