Layout-based Causal Inference for Object Navigation
Abstract
Previous works for ObjectNav task attempt to learn the association (e.g. relation graph) between the visual inputs and the goal during training. Such association contains the prior knowledge of navigating in training environments, which is denoted as the experience. The experience performs a positive effect on helping the agent infer the likely location of the goal when the layout gap between the unseen environments of the test and the prior knowledge obtained in training is minor. However, when the layout gap is significant, the experience exerts a negative effect on navigation. Motivated by keeping the positive effect and removing the negative effect of the experience, we propose the layout-based soft Total Direct Effect (L-sTDE) framework based on the causal inference to adjust the prediction of the navigation policy. In particular, we propose to calculate the layout gap which is defined as the KL divergence between the posterior and the prior distribution of the object layout. Then the sTDE is proposed to appropriately control the effect of the experience based on the layout gap. Experimental results on AI2THOR, RoboTHOR, and Habitat demonstrate the effectiveness of our method. The code is available at https://github.com/sx-zhang/Layout-based-sTDE.git.
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L-sTDE applies counterfactual causal inference to remove object layout mismatches between the current scene and prior knowledge, improving navigation policy generalization.
“In the context of embodied navigation, L-sTDE [67] utilizes causal inference based on counterfactual prediction to eliminate object layout discrepancies between the current scene and prior knowledge, enhancing the generalization of the navigation policy.”
From Unbiased Embodied Visual Representation Learning with Causal Inference and Cross-Modality Alignment (2025) · cites this paper as [67]
Cites
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“[38]) to analyze these navigation works.”
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RoboTHOR: An Open Simulation-to-Real Embodied AI Platform
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GridMM: Grid Memory Map for Vision-and-Language Navigation
Wang, Z., Li, X., Yang, J., Liu, Y., & Jiang, S. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01432 · Cited by 93 · see citing works
Self-citationUnbiased Embodied Visual Representation Learning with Causal Inference and Cross-Modality Alignment
Kang, J., Chen, B., Zhong, P., Wang, Y., Yang, H., & Sheng, Y. (2025) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3760261
- Authors
- Zhang, S., Song, X., Li, W., Bai, Y., Yu, X., & Jiang, S.
- Affiliations
- Chinese Academy of Sciences (CN)
- Institute of Computing Technology,Key Lab of Intelligent Information Processing Laboratory of the Chinese Academy of Sciences (CAS),Beijing
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 10792-10802
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386076477
- Cited by
- 41
- References
- 9
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 15 · 2023: 5
- FWCI
- 7.26
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- object navigation, causal inference, Kullback-Leibler divergence, object layout, unknown environments, relation graph, navigation policy
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