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.
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.
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.
naperlou, not everyone thinks about how a robot would do things they themselves are doing. But that does sound like how engineers think. Thanks for the observation about the lack of sensors here--I think that's a good point, and it's interesting to know this isn't the only research team taking that approach.
Glenn, thanks for that observation about the photo. I should have pointed out in the caption that this universal gripper, without the algorithm, can pick up objects but that this shows how it does so in a non-optimal manner, forming a "before" picture.
Ann R Thryft; Yes, optimal vs. non-optimal is the clarification. For some applications the optimal gripper is vacuum cup(s). The human hand is a very versatile end effector. Duplicating it is not easy. There could be applications where this gripper would be optimal, but I don't think the water glass is one of them.
Right now this looks like a technology development seeking a solution. As the robots develop, solutions will appear. I've seen this notion of robots learning how to do things by trial and error. That's impressive.
The point here is that, with a less expensive universal gripper, such as Cornell's, plus the algorithm the team invented, a robotic assembly line can quickly adapt to optimally picking up all kinds of new objects with different sizes and shapes that it's never encountered before. The alternative, which we've heard a lot about in DN articles and comments, is lengthy and expensive programming in 4D, presumably with highly specialized grippers. This would be a big benefit in assembly lines, especially those of EMS, which are continually changing products.
I have had a bit of experience with assembly lines. I can't think of any application for this gripper. Printed circuit board assembly needs very fast small part placement with vision compensation, or fast very fine placement of large parts with many leads, using vision compensation. I have only seen vacuum nozzles used. I can't see this gripper being used in a high-speed vision application. In automotive speed, accuracy, and payload are important. I don't think this gripper has any of these 3. Even where I have seen off-line programming using 3-D modeling, an actual human had to step through the program to touch-up positions and movements. Robots, aka Flexible Automation, vs. 'hard automation', was the answer to changing products. The gripper or 'end effector' is always customized to the application. The part must be both 'picked' and 'placed'.
To belabor the point: I don't think this gripper could pick up a 1mm x 2mm chip, take a vision shot, and then place it into a solder screened location, and do it again 1/10 second later. I also doubt that it could pick up a 50 lb bag of flour and place it to a pallet.
The gripper and the algorithm are interesting research, without a current practical application.
I like the ability of the robot to adapt and learn according to it's memory or history. no matter what the robot or technology in the robot I just love to hear about how the SW is learning.
I really liked the article. I don't know if I completely understand the inner workings of the pressure adaping inside of the big blue ball, but the statistics of success for picking up parts is pretty cool.
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