A robot gripper hand, invented by engineers at Cornell University, has inspired some of the same researchers to write an algorithm that can teach any robot how to pick up oddly-shaped objects.
The algorithm allows a robot to learn complex grasping skills by trial and error. The robot can then apply what it has learned in situations it hasn't previously encountered. The researchers said the method is hardware agnostic and will work with any type of robot gripper. This could be especially useful for autonomous industrial robots used in assembly or palletizing lines.
An algorithm inspired by Cornell University's universal jamming gripper robot hand, shown here picking up a glass of water, can teach any industrial robot how to pick up unfamiliar, oddly-shaped objects. (Source: John Amend, Cornell University)
The original "universal jamming gripper" hand was developed in October 2010 by the Cornell Creative Machines Lab headed by Hod Lipson, associate professor of mechanical engineering and computer science. It consists of a large latex balloon filled with a granular material, such as ground coffee. By modulating the air pressure inside the balloon, the granular material can quickly harden or soften to adapt to the object the arm is attempting to grasp.
The new algorithm was developed by Lipson and Ashutosh Saxena, Cornell assistant professor of computer science and a specialist in machine learning. Using the algorithm, a robot employs a 3D image of an object to examine several rectangles that match the size of the gripper. The robot tests each rectangle on a variety of features, and is also trained with images of different objects.
During this process, the robot builds up a library of features that are common to the properties of "good-grasping" rectangles. When the robot is given a new object, it chooses the rectangle that has scored the highest, based on the rules it has developed for good grasping. The robot also considers the overall size and shape of the object to help it choose a stable grasping point.
Picking up an object is only part of the problem. The picture shows a gripper spilling a glass of water. After the object is grasped, some purpose must be accomplished. If the water were wine and needed to go from a pitcher into a glass, it would be inportant not to spill it onto the floor or table, and that the robot's 'fingers' not get into the wine. While this is an interesting line of research, I can't see it replacing purpose-built grippers yet.
Ann, this might mark me out as a bit wierd, but I think about this a lot. Whenever I put the silverware away I thnk to myself, how would I program a robot to do this?
What really strikes me about this, and some other situations I have seen, is that people are programming robots to do things using a fairly simple vision system along with memory (a database) and an algorithm. This contrasts with robotics approaches that use all kinds of complex sensors. In many cases they are trying to automate something we do with our simple sensors naturally. Interesting.
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