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.
Watch the universal jamming gripper work here