To test the algorithm, the researchers fitted the jamming gripper and a Microsoft Kinect 3D camera onto an industrial robot arm. The robot was tested in attempts to pick up 23 different objects, including toys, tools, and dishes. In these tests, the robot's success rate averaged 90 percent to 100 percent.
In most cases, the robot arm could successfully grasp new objects that it had not reviewed during its training. When the team ran the same tests with a simple directive to pick up an object at its center, the robot arm scored only had a 30 percent to 50 percent success rate. The exception was in picking up flat objects. With these, both the learned grasps and simple center grasps tied at an 89 percent success rate. The algorithm was also tested with the standard parallel jaws that most modern robots use, which produced similar results.
The team, which includes graduate students Yun Jiang and John Amend, presented their results May 16 in a paper at the International Conference on Robotics and Automation in St. Paul, Minn.
Agree....Most of the comments are based on environments where uniform parts are pre-aligned. Many times that's fine, but what if electronic components, gears, etc. could be "loose" and gripped and oriented by more sophisticated robotics? It could result in net savings. Another application is when the component shapes or orientation are irregular and poorly defined- logs, chicken wings, gemstones, or debris on the seabed.
Jack, that's a good point about the use case of slight changes in the expected location of the object to be picked up. The main advantage the researchers cited was in adapting to different shaped and oddly shaped objects and being able to pick them up without dropping them (or spilling water from them as shown in the photo).
Yes, Ann, the adaptation to different shapes is the key component of the algorithm. I see two practical applications for something like that. First, it gives the robot a much higher margin of error when moving a product. If the product is not quite in the right orientation or has moved somewhat from where it it expected, the gripper can still get it (within reason). The second application is if the product the robot is trying to grab gets redesigned. A minor modification to it physical shape may not require as drastic of changes to the processes if the robot is still able to adapt to it.
Ann, Good story. These kinds of developments are important to continuing innovation. Nothing like new ideas to keep things moving ahead. Maybe this can spur specific application developments. Thanks.
jmiller, you can see and hear about the inner workings of the gripper's ball--what makes it a jamming gripper--in the video linked to in the article. The fact that the robot has to follow the same repetitive steps is secondary here: it's the fact that it may have to adjust those repetitive steps to different shaped objects, as stated in the article. That's what the algorithm teaches it to adapt to.
This gripper--which is not the main subject of the DN article--is not designed to pick and place small chips or other tiny objects on a high-speed line. The universal jamming gripper is a very different gripper designed to quickly grasp and release, or throw, a wide variety of object shapes. According to a FAQ http://creativemachines.cornell.edu/jamming_faq_2 for an earlier IEEE article about this gripper by its inventors, not the algorithm which my article focused on, specific applications include "military robotics and improvised explosive device (IED) defeat missions; consumer and service robotics in unstructured environments like the home; and industrial and manufacturing robotics able to perform of a wider variety of gripping tasks than currently possible." According to that article, universal grippers can be used for sorting and throwing objects. One immediate use that comes to mind is end-of-line palletizing for non-fragile objects. A different (non-jamming) approach to universal grippers is shown here: http://blog.robotiq.com/bid/29474/Universal-Gripper-Tooling-for-Pre-Engineered-Robotic-Cells
The human hand seems to be the model to mimic, but we still use tools - specialty grippers if you will. A totally universal gripper isn't there in nature either.
Logic is key. We can manipulate something a stick if needed, but of course much easier with the hand.
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