Multi-Object Navigation Using Potential Target Position Policy Function
文献类型：期刊论文
| 作者 | Zeng, Haitao; Song, Xinhang; Jiang, Shuqiang |
| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| 出版日期 | 2023 |
| 卷号 | 32页码:2608-2619 |
| 关键词 | Navigation Task analysis Semantics Visualization Reinforcement learning Trajectory Three-dimensional displays Multi-object navigation object navigation embodied AI |
| ISSN号 | 1057-7149 |
| DOI | 10.1109/TIP.2023.3263110 |
| 英文摘要 | Visual object navigation is an essential task of embodied AI, which is letting the agent navigate to the goal object under the user's demand. Previous methods often focus on single-object navigation. However, in real life, human demands are generally continuous and multiple, requiring the agent to implement multiple tasks in sequence. These demands can be addressed by repeatedly performing previous single task methods. However, by dividing multiple tasks into several independent tasks to perform, without the global optimization between different tasks, the agents' trajectories may overlap, reducing the efficiency of navigation. In this paper, we propose an efficient reinforcement learning framework with a hybrid policy for multi-object navigation, aiming to maximally eliminate noneffective actions. First, the visual observations are embedded to detect the semantic entities (such as objects). And the detected objects are memorized and projected into semantic maps, which can also be regarded as a long-term memory of the observed environment. Then a hybrid policy consisting of exploration and long-term planning strategies is proposed to predict the potential target position. In particular, when the target is directly oriented, the policy function makes long-term planning for the target based on the semantic map, which is implemented by a sequence of motion actions. In the alternative, when the target is not oriented, the policy function estimates an object's potential position toward exploring the most possible objects (positions) that have close relations to the target. The relation between different objects is obtained with prior knowledge, which is used to predict the potential target position by integrating with the memorized semantic map. And then a path to the potential target is planned by the policy function. We evaluate our proposed method on two large-scale 3D realistic environment datasets, Gibson and Matterport3D, and the experimental results demonstrate the effectiveness and generalization of the proposed method. |
| 资助项目 | National Key Research and Development Project of New Generation Artificial Intelligence of China[2018AAA0102500] ; National Natural Science Foundation of China[62125207] ; National Natural Science Foundation of China[62032022] ; National Natural Science Foundation of China[62272443] ; National Natural Science Foundation of China[U1936203] ; Beijing Natural Science Foundation[Z190020] ; Beijing Natural Science Foundation[JQ22012] ; National Postdoctoral Program for Innovative Talents[BX201700255] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000982402100010 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/21415]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Jiang, Shuqiang |
| 作者单位 | Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zeng, Haitao,Song, Xinhang,Jiang, Shuqiang. Multi-Object Navigation Using Potential Target Position Policy Function[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2023,32:2608-2619. |
| APA | Zeng, Haitao,Song, Xinhang,&Jiang, Shuqiang.(2023).Multi-Object Navigation Using Potential Target Position Policy Function.IEEE TRANSACTIONS ON IMAGE PROCESSING,32,2608-2619. |
| MLA | Zeng, Haitao,et al."Multi-Object Navigation Using Potential Target Position Policy Function".IEEE TRANSACTIONS ON IMAGE PROCESSING 32(2023):2608-2619. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。