Suction based robotic actuators have potential for the bin-picking industry, but are currently not usable due the needed speed, accuracy and ability to handle novel and adversarial objects. An evaluation of the state of the art grasp pipeline developed by Mahler et al. [1] for de
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Suction based robotic actuators have potential for the bin-picking industry, but are currently not usable due the needed speed, accuracy and ability to handle novel and adversarial objects. An evaluation of the state of the art grasp pipeline developed by Mahler et al. [1] for detecting grasps on novel objects lead us to split the problem of robotic grasp generation into a global and a local component. The state of the art solution had the ability to fill the role of global evaluator leaving a local evaluator to be developed. This local grasp evaluator was obtained by training a neural network on a suction based grasp dataset, which was created using a newly developed annotation tool. The proposed grasp pipeline obtained by combining these two showed a 95.27% pick success rate for a random setup and a 90.28% success rate for solely novel objects.
