Learning to Work in a Materials Recovery Facility: Can Humans and Machines Learn from Each Other?
Learning to Work in a Materials Recovery Facility: Can Humans and Machines Learn from Each Other?
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学习在材料回收设施中工作：人类和机器可以互相学习吗？
DOI：
10.1145/3448139.3448183
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Whitehill, Jacob
中科院分区：
文献类型：
--
作者：
Kyriacou, Harrison;Ramakrishnan, Anand;Whitehill, Jacob
工作场所学习通常要求工人学习新的感知和运动技能。未来的工作将越来越多地以人类用户与机器合作为特征，既学习任务又执行任务。在本文中，我们研究了材料回收设施（MRF）的工作场所学习，即，回收工厂，工人们在传送带上分离废物，然后将其打包并进行再处理。使用模拟的MRF，我们探索了机器学习助手（MLAs）的好处，它可以帮助工人并帮助他们通过提供自动感知指导来有效地对对象进行分类。在一个随机实验（n = 140）中，我们发现：（1）低准确性MLA比没有MLA更差，无论是在任务性能和学习。(2)一个完美的MLA会带来最好的任务性能，但在帮助用户学习方面并不比没有MLA更好。(3)用户往往过于频繁地遵循MLA的判断，即使它们是不正确的。最后，（4）我们设计了一种新的学习分析算法来评估工人的准确性，目的是获得可用于微调机器的额外训练标签。一项模拟研究表明，即使是嘈杂的标签也可以提高机器的准确性。
Workplace learning often requires workers to learn new perceptual and motor skills. The future of work will increasingly feature human users who cooperate with machines, both to learn the tasks and to perform them. In this paper, we examine workplace learning in Materials Recovery Facilities (MRFs), i.e., recycling plants, where workers separate waste items on conveyer belts before they are formed into bales and reprocessed. Using a simulated MRF, we explored the benefit of machine learning assistants (MLAs) that help workers, and help train them, to sort objects efficiently by providing automated perceptual guidance. In a randomized experiment (n = 140), we found: (1) A low-accuracy MLA is worse than no MLA at all, both in terms of task performance and learning. (2) A perfect MLA led to the best task performance, but was no better in helping users to learn than having no MLA at all. (3) Users tend to follow the MLA’s judgments too often, even when they were incorrect. Finally, (4) we devised a novel learning analytics algorithm to assess the worker’s accuracy, with the goal of obtaining additional training labels that can be used for fine-tuning the machine. A simulation study illustrates how even noisy labels can increase the machine’s accuracy.
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DOI：
10.1109/ro-man46459.2019.8956455
发表时间：
2019
期刊：
2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)
影响因子：
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作者：
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影响因子：
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作者：
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