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--- |
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license: cc-by-nc-sa-4.0 |
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library_name: transformers |
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pipeline_tag: robotics |
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--- |
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# VL-LN-Bench basemodel |
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This repository contains the base model for the paper [VL-LN Bench: Towards Long-horizon Goal-oriented Navigation with Active Dialogs](https://huggingface.co/papers/2512.22342). |
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## Model Description |
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VL-LN Bench is the first benchmark for **Interactive Instance Object Navigation (IION)**, where an embodied agent must locate a specific object instance in a realistic 3D home while engaging in **free-form natural-language dialogue**. It also provides an **automated data-collection pipeline** that generates large-scale training data for learning interactive navigation behaviors. Using this dataset, we train an **IION base model** that shares the same architecture as **InternVLA-N1**. |
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The resulting model demonstrates baseline competence on IION: it can search for a specific instance in **previously unseen** environments. During exploration, the agent can either **move** by predicting a pixel-goal waypoint or **ask** a question to reduce ambiguity and improve task success and efficiency. |
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### Resources |
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[](https://github.com/InternRobotics/InternNav) |
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[](https://arxiv.org/abs/2512.22342) |
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[](https://0309hws.github.io/VL-LN.github.io/) |
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[](https://huggingface.co/datasets/InternRobotics/InternData-N1) |
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## Usage |
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For inference and evaluation, please refer to the [VL-LN-Bench repository](https://github.com/InternRobotics/InternNav). |
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## Citation |
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If you find our work helpful, please cite: |
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```bibtex |
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@misc{huang2025vllnbenchlonghorizongoaloriented, |
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title={VL-LN Bench: Towards Long-horizon Goal-oriented Navigation with Active Dialogs}, |
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author={Wensi Huang and Shaohao Zhu and Meng Wei and Jinming Xu and Xihui Liu and Hanqing Wang and Tai Wang and Feng Zhao and Jiangmiao Pang}, |
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year={2025}, |
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eprint={2512.22342}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.RO}, |
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url={https://arxiv.org/abs/2512.22342}, |
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} |
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``` |