Active learning is a decision-making process. In both abstract and physical settings, active learning demands both analysis and action. This is a review of active learning in robotics, focusing on methods amenable to the demands of embodied learning systems. Robots must be able to learn efficiently and flexibly through continuous online deployment. This poses a distinct set of control-oriented challenges -- one must choose suitable measures as objectives, synthesize real-time control, and produce analyses that guarantee performance and safety with limited knowledge of the environment or robot itself. In this work, we survey the fundamental components of robotic active learning systems. We discuss classes of learning tasks that robots typically encounter, measures with which they gauge the information content of observations, and algorithms for generating action plans. Moreover, we provide a variety of examples -- from environmental mapping to nonparametric shape estimation -- that highlight the qualitative differences between learning tasks, information measures, and control techniques. We conclude with a discussion of control-oriented open challenges, including safety-constrained learning and distributed learning.
翻译:积极学习是一个决策进程。在抽象和物理环境中,积极学习需要分析和行动。这是对机器人积极学习的回顾,重点是符合成文学习系统要求的方法。机器人必须能够通过持续的在线部署来高效和灵活地学习。这提出了一套不同的面向控制的挑战 -- -- 我们必须选择适当的措施作为目标,综合实时控制,并进行分析,以有限的环境或机器人本身知识来保证性能和安全。在这项工作中,我们调查机器人积极学习系统的基本组成部分。我们讨论了机器人通常遇到的学习任务类别、他们衡量观测内容的措施以及生成行动计划的算法。此外,我们提供了从环境测绘到非参数形状估计等各种例子,这些例子突出了学习任务、信息计量和控制技术之间的质差异。我们最后讨论了以控制为导向的公开挑战,包括安全限制的学习和分布式学习。