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"We have been able to program robots, in very controlled environments, to pick up very specific items. However, one of the key shortcomings of current robotic grasping systems is the inability to quickly adapt to change, such as when an object gets moved," Dr. Leitner said.
"The Generative Grasping Convolutional Neural Network approach works by predicting the quality and pose of a two-fingered grasp at every pixel. By mapping what is in front of it using a depth image in a single pass, the robot doesn't need to sample many different possible grasps before making a decision, avoiding long computing times," Mr. Morrison said.
"In our real-world tests, we achieved an 83% grasp success rate on a set ofpreviously unseen objects with adversarial geometry and 88% on a set ofhousehold objects that were moved during the grasp attempt. We also achieve81% accuracy when grasping in dynamic clutter."
The team's paper Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach will be presented this week at Robotics: Science and Systems, the most selective international robotics conference, which is being held at Carnegie Mellon University in Pittsburgh USA. The research was supported by the Australian Centre for Robotic Vision
