@article{Wang2026, 
author = {Yuheng Wang and Jiayi Li and Zihao Du and Tao Xue and Tao Zhang},
title = {Algorithmic Perspectives on Active Object Recognition Systems: A Survey},
year = {2026},
journal = {Tsinghua Science and Technology},
volume = {31},
number = {3},
pages = {1307-1325},
keywords = {active perception, Active Object Recognition (AOR), robotics, sensor planning, robot learning, uncertainty},
url = {https://www.sciopen.com/article/10.26599/TST.2025.9010096},
doi = {10.26599/TST.2025.9010096},
abstract = {With the continuous advancement of artificial intelligence technology, traditional passive sensing methods have encountered certain limitations. Some downstream tasks require sensors to acquire information actively. Against this backdrop, active perception technology has made significant progress in recent years. This article uses specific algorithms as a classification dimension for various Active Object Recognition (AOR) tasks, providing a relatively comprehensive introduction to the development of active object recognition within active perception. Furthermore, the works and methods introduced in this article are not restricted to any particular type of sensor. Instead, they focus on commonalities across AOR tasks based on different sensors—specifically, how to plan the subsequent actions of sensors. Additionally, the paper introduces commonly used datasets, applications, and potential future developments within the field of AOR.}
}