Hang Yin

Hang Yin | 尹航

Hang Yin is currently a PhD student in the Department of Automation, Tsinghua University. His research interests include computer vision and embodied navigation.

Email / CV / Google Scholar / Github

News

2026-06: One paper on lifelong navigation is released on arXiv.

2025-08: One paper on vision-and-language navigation is accepted to CoRL 2025.

2025-02: One paper on goal-oriented navigation is accepted to CVPR 2025.

2024-09: One paper on object-goal navigation is accepted to NeurIPS 2024.

Publications

	AllDayNav: Lifelong Navigation via Real-World Reinforcement Learning Hang Yin, Yinan Liang, Jiazhao Zhang, Jiahang Liu, Minghan Li, Zhizheng Zhang^†, He Wang^† arXiv*, 2026 [arXiv] [Project Page] We propose AllDayNav, a lifelong navigation system via real-world reinforcement learning. The robot autonomously builds a self-evolving multimodal memory, generates self-instructions, and continuously improves its navigation policy without human supervision, achieving near-100% success rates.
	GC-VLN: Instruction as Graph Constraints for Training-free Vision-and-Language Navigation Hang Yin, Haoyu Wei, Xiuwei Xu^†, Wenxuan Guo, Jie Zhou, Jiwen Lu Conference on Robot Learning (CoRL), 2025 [arXiv] [Code] [Project Page] We propose a training-free framework for vision-and-language navigation. Our framework formulates navigation guidance as graph constraint optimization by decomposing instructions into explicit spatial constraints, enabling zero-shot adaptation to unseen environments.
	UniGoal: Towards Universal Zero-shot Goal-oriented Navigation Hang Yin, Xiuwei Xu^†, Linqing Zhao, Ziwei Wang, Jie Zhou, Jiwen Lu IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025 [arXiv] [Code] [Project Page] [中文解读] We propose UniGoal, a unified graph representation for zero-shot goal-oriented navigation. Based on online 3D scene graph prompting for LLM, our method can be directly applied to different kinds of scenes and goals without training.
	SG-Nav: Online 3D Scene Graph Prompting for LLM-based Zero-shot Object Navigation Hang Yin, Xiuwei Xu^†, Zhenyu Wu, Jie Zhou, Jiwen Lu Thirty-eighth Conference on Neural Information Processing Systems (NeurIPS), 2024 [arXiv] [Code] [Project Page] [中文解读] We propose a training-free object-goal navigation framework by leveraging LLM and VFMs. We construct an online hierarchical 3D scene graph and prompt LLM to exploit structure information contained in subgraphs for zero-shot decision making.

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