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Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss

Prew, W.; Breckon, T.P.; Bordewich, M.J.R.; Beierholm, U.

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Abstract

In this paper we introduce two methods of improving real-time object grasping performance from monocular colour images in an end-to-end CNN architecture. The first is the addition of an auxiliary task during model training (multi-task learning). Our multi-task CNN model improves grasping performance from a baseline average of 72.04% to 78.14% on the large Jacquard grasping dataset when performing a supplementary depth reconstruction task. The second is introducing a positional loss function that emphasises loss per pixel for secondary parameters (gripper angle and width) only on points of an object where a successful grasp can take place. This increases performance from a baseline average of 72.04% to 78.92% as well as reducing the number of training epochs required. These methods can be also performed in tandem resulting in a further performance increase to 79.12%, while maintaining sufficient inference speed to afford real-time grasp processing.

Citation

Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2021). Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss. . https://doi.org/10.1109/icpr48806.2021.9413197

Conference Name 25th International Conference on Pattern Recognition (ICPR 2020)
Conference Location Milan, Italy
Start Date Jan 10, 2021
End Date Jan 15, 2021
Acceptance Date Oct 11, 2020
Online Publication Date May 5, 2021
Publication Date 2021
Deposit Date Oct 25, 2020
Publicly Available Date Oct 27, 2020
Series ISSN 1051-4651
DOI https://doi.org/10.1109/icpr48806.2021.9413197

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