用自监督学习让机器人自主标注抓取数据，减少人工干预。
Self Supervised Deep Learning for Robot Grasping
- 机器人在训练中自动采集并标注抓取数据，无需人工标注
- 通过数百小时训练，实现从小型实验机器人到大型机器人的模型迁移
- 适合希望降低数据标注成本的机器人研发团队
基于学习的机器人抓取目前依赖标注数据，存在两大问题：一是抓取点和角度的标注过程繁琐，导致数据集有限；二是人工标注易受语义偏差影响。为解决这些问题，我们提出一种更简单的自监督机器人设置，可训练卷积神经网络（CNN）。机器人在训练过程中自行标注并收集数据，旨在构建低成本、小型且易于实验室维护的系统。该机器人将在大规模数据集上训练数百小时，之后将训练好的神经网络迁移到更大规模的抓取机器人上。
原文摘要 · Abstract (English)
Learning Based Robot Grasping currently involves the use of labeled data. This approach has two major disadvantages. Firstly, labeling data for grasp points and angles is a strenuous process, so the dataset remains limited. Secondly, human labeling is prone to bias due to semantics. In order to solve these problems we propose a simpler self-supervised robotic setup, that will train a Convolutional Neural Network (CNN). The robot will label and collect the data during the training process. The idea is to make a robot that is less costly, small and easily maintainable in a lab setup. The robot will be trained on a large data set for several hundred hours and then the trained Neural Network can be mapped onto a larger grasping robot.
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