Toward Automatic Robot Instruction from Perception: Recognizing a Grasp from Observation

Abstract

Deals with 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: pregrasp phase, static grasp phase, and manipulation phase. The first step in recognizing a grasping task is identifying the grasp itself. The proposed strategy of identifying the grasp is to map the low-level hand configuration to increasingly more abstract grasp descriptions. To achieve the mapping, a grasp representation is introduced, called the contact web, which is composed of a pattern of effective contact 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 that 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.