Many of the tasks that are potential candidates for automation involve grasping. The authors are interested in the programming of robots to perform grasping tasks. To do this, the Assembly Plan from Observation (APO) paradigm is adopted, where the key idea is to enable a system to observe a human performing a grasping task, understand it, and perform the task with minimal human intervention.

A grasping task is composed of three phases: pre-grasp phase, static grasp phase, and manipulation phase. The first step in recognizing a grasping task is identifying the grasp itself (within the static grasp phase).

The proposed strategy of identifying the grasp is to map the low-level hand configuration to increasingly more abstract grasp descriptions. The abstract grasp descriptions are useful because they are manipulator-independent. To achieve the mapping, a grasp representation is introduced that is called the contact web, which is composed of a pattern of effective con tact points between the hand and the object. A grasp taxonomy based on the contact web is also proposed as a tool to systematically identify a grasp. The grasp can be described at higher conceptual levels using a certain mapping function which results in an index called the grasp cohesive index. This index can be used to identify the grasp. Results from grasping experiments show that it is possible to distinguish between various types of grasps using the proposed contact web, grasp taxonomy and grasp cohesive index.