Review of ``Robust grasping under object pose uncertainty'' by Kaijen Hsaio, Leslie Pack Kaelbling, and Tomas Lozano-Perez, Autonomous Robots, 2011, 31:253-268. Review #CR140061 in Computing Reviews April 17, 2012.
The apparently simple act of moving a robot manipulator so as to grasp a target object can involve many complex issues, particularly when the position of the object is imperfectly known.
The research described in this paper considers a number of different interacting issues that complicate this task. First, the position of the object is known only imperfectly; a probability distribution over a range is given, presumably obtained from a vision system. New information is gathered as a result of moving the arm toward the object, either by finding a contact or by not finding a contact. Second, collisions between the arm and the object may result in moving the object; this can either happen accidentally, or it can be the result of a deliberate attempt to move the object. Third, movements are considered with three kinds of goals; to actually achieve the grasp; to rotate the object into a position where the desired grasp is feasible; or to gather information about the position of the object.
The technique for planning these motions use a decision-theoretic framework. A belief state records a probability distribution for the position of the object; this guides the choice of motion, and is updated as more information is acquired. A library of motions, described in terms of the object's frame of reference, of each of the three categories of goals, is compiled off-line, partly by manual training and partly by simulation.
The combinatorics both of belief update and of choosing a proper action soon become daunting; the major technical issue addressed in this research is the development of strategies to keep these manageable.
The system has been extensively tested on a simulated robot, and to a limited degree on a real robot, and has achieved a impressive degree of success. It is a substantial contribution to our understanding of techniques for planning robotic manipulations.