Grasping Student: semi-supervised learning for robotic manipulation

• URL: http://arxiv.org/abs/2303.04452v1
• Date: Wed, 8 Mar 2023 09:03:11 GMT
• Title: Grasping Student: semi-supervised learning for robotic manipulation
• Authors: Piotr Krzywicki, Krzysztof Ciebiera, Rafa{\l} Michaluk, Inga Maziarz, Marek Cygan
• Abstract summary: We design a semi-supervised grasping system that takes advantage of images of products to be picked, which are collected without any interactions with the robot. In the regime of a small number of robot training samples, taking advantage of the unlabeled data allows us to achieve performance at the level of 10-fold bigger dataset size.
• Score: 0.7282230325785884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gathering real-world data from the robot quickly becomes a bottleneck when constructing a robot learning system for grasping. In this work, we design a semi-supervised grasping system that, on top of a small sample of robot experience, takes advantage of images of products to be picked, which are collected without any interactions with the robot. We validate our findings both in the simulation and in the real world. In the regime of a small number of robot training samples, taking advantage of the unlabeled data allows us to achieve performance at the level of 10-fold bigger dataset size used by the baseline. The code and datasets used in the paper will be released at https://github.com/nomagiclab/grasping-student.

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