Towards a Robot Architecture for Situated Lifelong Object Learning
Towards a Robot Architecture for Situated Lifelong Object Learning
复制标题
面向情境终身对象学习的机器人架构
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Oliver Lemon
中科院分区：
文献类型：
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作者：
Jose L. Part;Oliver Lemon
对于在真实的世界中运行的机器人来说，增量地和在部署之后获取知识的能力是至关重要的。此外，必须与人一起操作的机器人需要能够以用户直观的方式进行交互，例如，通过理解和创造自然语言。在本文中，我们提出了第一个原型的机器人体系结构的终身对象学习。该系统能够通过自然语言与用户进行交流，并通过现场互动进行对象学习和识别。在第一阶段，我们根据识别准确性评估系统，这是对使用建议管道收集的数据质量的间接测量。我们的研究结果表明，机器人可以使用这些数据进行学习和识别，并具有可接受的增量性能。我们还讨论了限制和必要的步骤，以提高性能，以及阐明系统的可用性。
The ability to acquire knowledge incrementally and after deployment is of utmost importance for robots operating in the real world. Moreover, robots that have to operate alongside people need to be able to interact in a way that is intuitive for the users, e.g., by understanding and producing natural language. In this paper we present a first prototype of a robot architecture developed for situated lifelong object learning. The system is able to communicate with its users through natural language and perform object learning and recognition on the spot through situated interactions. In this first stage, we evaluate the system in terms of recognition accuracy which gives an indirect measure of the quality of the collected data with the proposed pipeline. Our results show that the robot can use this data for both learning and recognition with acceptable incremental performance. We also discuss limitations and steps that are necessary in order to improve performance as well as to shed some light on system usability.
移动机器人上对象模型的自主学习
影响因子：
5.2
作者：
Faulhammer T
通讯作者：
Faulhammer T