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Pré-Publication, Document De Travail Année : 2021

Scoring Graspability based on Grasp Regression for Better Grasp Prediction

Résumé

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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Dates et versions

hal-02456956 , version 1 (31-01-2020)
hal-02456956 , version 2 (29-03-2021)
hal-02456956 , version 3 (31-03-2021)

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Amaury Depierre, Emmanuel Dellandréa, Liming Chen. Scoring Graspability based on Grasp Regression for Better Grasp Prediction. 2021. ⟨hal-02456956v2⟩
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