Scoring Graspability based on Grasp Regression for Better Grasp Prediction
Amaury Depierre,Emmanuel Dellandréa,Liming Chen,Amaury Depierre,Emmanuel Dellandréa,Liming Chen
Grasping objects is one of the most important abilities that a robot needs to master in order to interact with its environment. Current state-of-the-art methods rely on deep neural networks trained to jointly predict a graspability score together with a regression of an offset with respect to grasp reference parameters. However, these two predictions are performed independently, which can lead to ...
