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      Optimizing Correlated Graspability Score and Grasp Regression for Better Grasp Prediction

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          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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          Author and article information

          Journal
          03 February 2020
          Article
          2002.00872
          d410bbdb-5c42-42ad-8e82-599af57f4be5

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Custom metadata
          cs.AI cs.RO
          ccsd

          Robotics,Artificial intelligence
          Robotics, Artificial intelligence

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