在有限人类监督的物理机器人上学习对象分类器

Christopher Eriksen, A. Nicolai, W. Smart
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引用次数: 4

摘要

近年来,人们利用深度学习方法在物体识别方面取得了令人印象深刻的成果。然而,由于收集和标记训练图像的负担,这种技术在现实世界的机器人应用中存在问题。我们提出了一个框架,通过该框架,我们可以指导机器人以很少的人力来获取领域相关数据。该框架位于终身学习范式中,通过该范式,机器人可以随着时间的推移更加智能地收集和存储数据。通过只在图像视图上进行迭代训练来提高分类器的性能,我们的方法能够收集具有代表性的对象视图,并且对数据集的长期存储的数据需求更少。我们表明,与使用可用的预构建数据集相比,我们获取领域相关数据的方法显著提高了对领域内对象的分类性能。此外,我们的迭代视图采样方法能够在分类器性能和数据存储约束之间找到一个很好的平衡。
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Learning Object Classifiers with Limited Human Supervision on a Physical Robot
In recent years, deep learning approaches have been leveraged to achieve impressive results in object recognition. However, such techniques are problematic in real world robotics applications because of the burden of collecting and labeling training images. We present a framework by which we can direct a robot to acquire domain-relevant data with little human effort. This framework is situated in a lifelong learning paradigm by which the robot can be more intelligent about how it collects and stores data over time. By iteratively training only on image views that increase classifier performance, our approach is able to collect representative views of objects with fewer data requirements for longterm storage of datasets. We show that our approach for acquiring domain-relevant data leads to a significant improvement in classification performance on in-domain objects compared to using available pre-constructed datasets. Additionally, our iterative view sampling method is able to find a good balance between classifier performance and data storage constraints.
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