Learning Hierarchical Graph Convolutional Neural Network for Object Navigation | |
Tao Xu2,3; Xu Yang2,3; Suiwu Zheng1,2,3 | |
2022-09 | |
会议日期 | 2022年9月6日-2022年9月9日 |
会议地点 | 西英格兰大学计算机科学与创新技术系 |
DOI | 10.1007/978-3-031-15931-2_45 |
英文摘要 | The goal of object navigation is to navigate an agent to a target object using visual input. Without GPS and the map, one challenge of this task is how to locate the target object in the unseen environment, especially when the target object is not in the field of view. Previous works use relation graphs to encode the concurrence relationships among all the object categories, but these relation graphs are usually too flat for the agent to locate the target object efficiently. In this paper, a Hier archical Graph Convolutional Neural Network (HGCNN) is proposed to encode the object relationships in a hierarchical manner. Specifically, the HGCNN consists of two graph convolution blocks and a graph pooling block, which constructs the hierarchical relation graph by learning an area-level graph from the object-level graph. Consequently, the HGCNN based framework enables the agent to locate the target object efficiently in the unseen environment. The proposed model is evaluated in the AI2-iTHOR environment, and the performance of object navigation shows a significant improvement. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/51847] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
通讯作者 | Suiwu Zheng |
作者单位 | 1.Huizhou Zhongke Advanced Manufacturing Limited Company, Huizhou, China 2.University of Chinese Academy of Sciences, Beijing, China 3.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Tao Xu,Xu Yang,Suiwu Zheng. Learning Hierarchical Graph Convolutional Neural Network for Object Navigation[C]. 见:. 西英格兰大学计算机科学与创新技术系. 2022年9月6日-2022年9月9日. |
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