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Yuting Xiao

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AAAI Conference 2026 Conference Paper

LOG-Nav: Efficient Layout-Aware Object-Goal Navigation with Hierarchical Planning

  • Jiawei Hou
  • Yuting Xiao
  • Xiangyang Xue
  • Taiping Zeng

We introduce LOG-Nav, an efficient layout-aware object-goal navigation approach designed for complex multi-room indoor environments. By planning hierarchically leveraging a global topologigal map with layout information and local imperative approach with detailed scene representation memory, LOG-Nav achieves both efficient and effective navigation. The process is managed by an LLM-powered agent, ensuring seamless effective planning and navigation, without the need for human interaction, complex rewards, or costly training. Our experimental results on the MP3D benchmark achieves 85% object navigation success rate (SR) and 79% success rate weighted by path length (SPL) (over 40% point improvement in SR and 60% improvement in SPL compared to exsisting methods). Furthermore, we validate the robustness of our approach through virtual agent and real-world robotic deployment, showcasing its capability in practical scenarios.

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AAAI Conference 2021 Conference Paper

Amodal Segmentation Based on Visible Region Segmentation and Shape Prior

  • Yuting Xiao
  • Yanyu Xu
  • Ziming Zhong
  • Weixin Luo
  • Jiawei Li
  • Shenghua Gao

Almost all existing amodal segmentation methods make the inferences of occluded regions by using features corresponding to the whole image. This is against the human’s amodal perception, where human uses the visible part and the shape prior knowledge of the target to infer the occluded region. To mimic the behavior of the human and solve the ambiguity in the learning, we propose a framework, it firstly estimates a coarse visible mask and a coarse amodal mask. Then based on the coarse prediction, our model infers the amodal mask by concentrating on the visible region and utilizing the shape prior in the memory. In this way, features corresponding to background and occlusion can be suppressed for amodal mask estimation. Consequently, the amodal mask would not be affected by the occlusion when given the same visible regions. The leverage of shape prior makes the amodal mask estimation more robust and reasonable. Our proposed model is evaluated on three datasets. Experiments show that our proposed model outperforms existing state-of-the-art methods. The visualization of shape prior indicates that the category-specific feature in the codebook has certain interpretability. The code is available at https: //github. com/YutingXiao/Amodal-Segmentation- Based-on-Visible-Region-Segmentation-and-Shape-Prior.

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