Robots make quick grab for objects in complex environments

The new approach from Queensland University of Technology allows a robot to quickly scan the environment and map each pixel it captures to its grasp quality using a depth image. In tests, the approach - which is based on a Generative Grasping Convolutional Neural Network - achieved accuracy rates of up to 88 per cent for dynamic grasping and up to 92 per cent in static experiments.

QUT’s Dr Jürgen Leitner said while grasping and picking up an object was a basic task for humans, it had proved incredibly difficult for machines.

“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,” he said. “The world is not predictable – things change and move and get mixed up and, often, that happens without warning – so robots need to be able to adapt and work in very unstructured environments if we want them to be effective.”