Institutional Repository of Coll Elect & Informat Engn
Target-Driven Visual Navigation Using Causal Intervention | |
Zhao, Xinzhou1; Wang, Tian2; Liu, Kexin3; Zhang, Baochang4; Li, Ce5; Snoussi, Hichem6 | |
2023 | |
会议名称 | 35th Chinese Control and Decision Conference (CCDC) |
会议录名称 | IEEE |
页码 | 3508-3513 |
会议日期 | MAY 20-22, 2023 |
会议地点 | Yichang, PEOPLES R CHINA |
出版地 | NEW YORK |
摘要 | Target-driven visual navigation has gained significance and presents great potentials in scientific and industrial fields. However, how to achieve faster convergence and better generalization is a challenging problem. One of the most critical hurdles is the neglect of confounders, which often leads to spurious correlations. Confounders make it difficult to discover the real causality and therefore are taken into consideration in some other fields. In this paper, we introduce a Causal Intervention Visual Navigation (CIVN) method, based on deep reinforcement learning and causal inference. We propose to realize causal intervention in navigation via front-door adjustment as most confounders are unobservable. Specifically, CIVN is implemented by Target-Related Shortcut, which serves as an approximation of causal intervention. To eliminate the confounding effect, we adapt cross-sampling and strengthen the target information. It is worth mentioning that causal intervention is for the first time applied by us in solving the confounding effect in target-driven visual navigation. Navigation results on AI2-THOR demonstrate that CIVN converges faster and achieves better evaluation performance than prior arts. Moreover, the generalization for unknown targets and scenes is also improved. |
关键词 | target-driven visual navigation causal intervention front-door adjustment |
DOI | 10.1109/CCDC58219.2023.10327097 |
收录类别 | CPCI-S |
语种 | 英语 |
WOS研究方向 | Automation & Control Systems ; Operations Research & Management Science |
WOS类目 | Automation & Control Systems ; Operations Research & Management Science |
WOS记录号 | WOS:001116704303127 |
原始文献类型 | Proceedings Paper |
引用统计 | 无
|
文献类型 | 会议论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/170212 |
专题 | 电气工程与信息工程学院 |
通讯作者 | Wang, Tian |
作者单位 | 1.Beihang Univ, Sch Automat Sci & Elect Engn, Beijing, Peoples R China; 2.Beihang Univ, Zhongguancun Lab, Inst Artificial Intelligence, SKLSDE, Beijing, Peoples R China; 3.Beihang Univ, Sch Automat Sci & Elect Engn, Zhongguancun Lab, Beijing, Peoples R China; 4.Beihang Univ, Inst Artificial Intelligence, Zhongguancun Lab, Beijing, Peoples R China; 5.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou, Peoples R China; 6.Univ Technol Troyes, Inst Charles Delaunay LM2S, Troyes, France |
推荐引用方式 GB/T 7714 | Zhao, Xinzhou,Wang, Tian,Liu, Kexin,et al. Target-Driven Visual Navigation Using Causal Intervention[C]. NEW YORK,2023:3508-3513. |
条目包含的文件 | 条目无相关文件。 |
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