Scoring Graspability based on Grasp Regression for Better Grasp Prediction

• URL: http://arxiv.org/abs/2002.00872v3
• Date: Wed, 31 Mar 2021 08:09:26 GMT
• Title: Scoring Graspability based on Grasp Regression for Better Grasp Prediction
• Authors: Amaury Depierre (imagine), Emmanuel Dellandr\'ea (imagine), Liming Chen (imagine)
• Abstract summary: 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. In this paper, we extend a state-of-the-art neural network with a scorer that evaluates the graspability of a given position, and introduce a novel loss function which correlates regression of grasp parameters with graspability score.
• Score: 2.835565391455372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 a decrease in the actual graspability score when applying the predicted offset. Therefore, in this paper, we extend a state-of-the-art neural network with a scorer that evaluates the graspability of a given position, and introduce a novel loss function which correlates regression of grasp parameters with graspability score. We show that this novel architecture improves performance from 82.13% for a state-of-the-art grasp detection network to 85.74% on Jacquard dataset. When the learned model is transferred onto a real robot, the proposed method correlating graspability and grasp regression achieves a 92.4% rate compared to 88.1% for the baseline trained without the correlation.

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