Learning on the Job : Improving Robot Perception Through Experience
Learning on the Job : Improving Robot Perception Through Experience
复制标题
在工作中学习：通过经验提高机器人感知
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
I. Posner
中科院分区：
文献类型：
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作者：
Corina Gurau;Jeffrey Hawke;Chi Hay Tong;I. Posner
本文介绍的是通过使用自主学习如何解释环境的机器人。具体来说，通过毫不费力的人类协助，机器人的感知能力在每次外出时都会得到改善。这是因为机器人在特定的应用领域中重复操作。我们的方法称为基于经验的分类（EBC），在精神上与硬负挖掘（HNM）的概念相似，但它完全是自我监督的。在自动驾驶的背景下，我们使用17公里的数据来证明EBC是HNM的实用替代方案，并展示了特定于体验的分类器的优势。我们相信，我们的方法代表了如何看待机器人感知的根本转变，并倡导在特定应用领域表现出色的终身学习系统，而不是在任何地方都表现平庸。
This paper is about robots that autonomously learn how to interpret their environment through use. Specifically, robot perception is improved with every outing through effortless human assistance. This is made possible by the fact that robots operate repeatedly in specific application domains. Our approach, which we call Experience-Based Classification (EBC), is similar in spirit to the concept of hard negative mining (HNM), but it is entirely self-supervised. In the context of autonomous driving we use 17km of data to show that EBC is a practical alternative to HNM, and demonstrate the advantages of experience-specific classifiers. We believe that our approach presents a fundamental shift in how robot perception is viewed, and advocate for lifelong learning systems that excel in a specific application domain instead of providing mediocre performance everywhere.