Method allows robot to learn and apply grasping skills
Cornell researchers claim to have developed a new algorithm that allows a robot to learn complex grasping skills from experience and to apply them in new situations.
Inspired by the ‘universal jamming gripper’ created in the lab of Hod Lipson, associate professor of mechanical engineering and computer science, the new method is ‘hardware agnostic’, according to the researchers, and will work with any type of robot gripper.
The work was done by Lipson and Ashutosh Saxena, assistant professor of computer science and a specialist in machine learning. It will be presented on 16 May at the International Conference on Robotics and Automation in St Paul, Minnesota. Co-authors of their paper are graduate students Yun Jiang and John Amend.
Lipson’s gripper is said to consist of a flexible bag filled with a granular material. With the new algorithm, the robot uses a 3D image of the object to examine a series of rectangles that match the size of the gripper and tests each one on a variety of features.
According to a statement, the robot is trained on images of many different objects until it has built up a library of features common to good-grasping rectangles.
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