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Optimizing Correlated Graspability Score and Grasp Regression for Better Grasp Prediction

Amaury Depierre 1, 2 Emmanuel Dellandréa 1 Liming Chen 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Grasping objects is one of the most important abilities to master for a robot in order to interact with its environment. Current state-of-the-art methods rely on deep neural networks trained to predict a graspability score jointly but separately from regression of an offset of grasp reference parameters, although the predicted offset could decrease the graspability score. In this paper, we extend a state-of-the-art neural network with a scorer which 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 the performance from 81.95% for a state-of-the-art grasp detection network to 85.74% on Jacquard dataset. Because real-life applications generally feature scenes of multiple objects laid on a variable decor, we also introduce Jacquard+, a test-only extension of Jacquard dataset. Its role is to complete the traditional real robot evaluation by benchmarking the adaptability of a learned grasp prediction model on a different data distribution than the training one while remaining in totally reproducible conditions. Using this novel benchmark and evaluated through the Simulated Grasp Trial criterion, our proposed model outperforms a state-of-the-art one by 7 points.
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Preprints, Working Papers, ...
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Contributor : Amaury Depierre <>
Submitted on : Friday, January 31, 2020 - 4:25:37 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:48 PM
Long-term archiving on: : Friday, May 1, 2020 - 12:31:36 PM


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  • HAL Id : hal-02456956, version 1
  • ARXIV : 2002.00872


Amaury Depierre, Emmanuel Dellandréa, Liming Chen. Optimizing Correlated Graspability Score and Grasp Regression for Better Grasp Prediction. 2020. ⟨hal-02456956⟩



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