'Kinova's robot is robust and easy to use (not forgetting its awesome software), making it the ideal platform to prototype from. The new Gen3 looks like it has super-sized in terms of hardware, packing an even more powerful punch as a research tool.'
Doug Morrison, PhD Researcher, Australian Centre for Robotic Vision
Significantly smaller and faster than other Convolutional Neural Networks, The Australian Centre for Robotic Vision’s GG-CNN achieved state-of-the-art results in grasping unknown, dynamic objects, including objects in cluttered and changing environments. The final GG-CNN contained 62,420 parameters, compared to CNNs used for grasp candidate classification in other works containing hundreds of thousands or millions of parameters.
Their network’s lightweight and single-pass generative nature allowed for closed-loop control at up to 50 Hz, enabling accurate grasping in non-static environments where objects move and in the presence of robot control inaccuracies.
83% grasp success rate on a set of previously unseen objects with adversarial geometry
88% success rate on a set of household objects moved during the grasp attempt
81% accuracy when grasping in dynamic clutter