Abstract
The classification and grasping of randomly placed objects where only a limited number of training images are available, remains a challenging problem. Approaches such as data synthesis have been used to synthetically create larger training data sets from a small set of training data and can be used to improve performance. This paper examines how limited product images for ‘off the shelf’ items can be used to generate a synthetic data set that is used to train a that allows classification of the item, segmentation and grasping. Experiments investigating the effects of data synthesis are presented and the subsequent trained network implemented in a robotic system to perform grasping of objects.
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Acknowledgments
With thanks to the James Dyson Foundation Undergraduate Bursary and also the EPSRC CDT in Sensor Technologies (Grant EP/L015889/1).
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Cheah, M., Hughes, J., Iida, F. (2018). Data Synthesization for Classification in Autonomous Robotic Grasping System Using ‘Catalogue’-Style Images. In: Giuliani, M., Assaf, T., Giannaccini, M. (eds) Towards Autonomous Robotic Systems. TAROS 2018. Lecture Notes in Computer Science(), vol 10965. Springer, Cham. https://doi.org/10.1007/978-3-319-96728-8_4
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DOI: https://doi.org/10.1007/978-3-319-96728-8_4
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