机器人增量学习研究综述

机器人的应用场景正在不断更新换代,数据量也在日益增长.传统的机器学习方法难以适应动态的环境,而增量学习技术能够模拟人类的学习过程,使机器人能利用旧知识来加快新任务的学习,在不遗忘旧技能的前提下学习新的技能.目前对于机器人增量学习的相关研究仍然较少,对此,主要介绍机器人增量学习研究进展.首先,对增量学习进行简介;其次,从参数和模型的角度出发,将当前机器人增量学习主流方法分为变参数方法、变模型方法、混合方法3类,分别对每一类进行论述,并给出相应的增量学习技术在机器人领域中的应用实例;然后,对机器人增量学习中常用的数据集和评价指标进行介绍;最后,对增量学习未来的发展趋势进行展望.

Incremental learning for robots: A survey

Nowadays, the application scenarios of robots are constantly updated, and the amount of data is also growing. Traditional machine learning methods are difficult to adapt to the dynamic environment. Incremental learning technology simulates the human learning process, enabling robots to use old knowledge to speed up the learning of new tasks and learn new skills without forgetting old skills. Currently, there is still relatively little research on robot incremental learning. This paper mainly introduces the research progress of robot incremental learning. Firstly, a brief introduction to incremental learning is given. Secondly, from the perspective of parameters and models, this paper classifies the current mainstream methods of robot incremental learning into three categories: variable parameter methods, variable model methods and hybrid methods, which are discussed in details, separately. Furthermore, the corresponding application examples of incremental learning technology in the field of robotics are provided. Thirdly, the data sets and evaluation metrics commonly used in incremental learning are introduced. Finally, the future development trends are prospected.