add VL-LN Bench doc

source/en/user_guide/internnav/index.md

@@ -13,4 +13,5 @@ myst:
 
 quick_start/index
 tutorials/index
+projects/index
 ```

source/en/user_guide/internnav/projects/benchmark.md

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+# Extended Benchmarks in InternNav
+
+This guide details how to use specific dataset for training a VLA model for different navigation benchmark.
+
+## VL-LN Bench
+
+VL-LN Bench is a large-scale benchmark for Interactive Instance Goal Navigation. VL-LN Bench provides: (1) an automatically dialog-augmented trajectory generation pipeline, (2) a comprehensive evaluation protocol for training and assessing dialog-capable navigation models, and (3) the dataset and base model used in our experiments. For full details, see our [paper](https://arxiv.org/abs/2512.22342) and the [project website](https://0309hws.github.io/VL-LN.github.io/).
+
+- [Data Collection Pipeline](https://github.com/InternRobotics/VL-LN)
+- [Training and Evaluation Code](https://github.com/InternRobotics/InternNav)
+- [Dataset](https://huggingface.co/datasets/InternRobotics/VL-LN-Bench) and [Base Model](https://huggingface.co/InternRobotics/VL-LN-Bench-basemodel)
+
+
+### 1. Download Data & Assets
+VL-LN Bench is built on Matterport3D (MP3D) Scene Dataset, so you need to download both the MP3D scene dataset and the VL-LN Bench dataset.
+- Scene Datasets
+
+    Download the [MP3D Scene Dataset](https://niessner.github.io/Matterport/)
+- [VL-LN Data](https://huggingface.co/datasets/InternRobotics/VL-LN-Bench)
+- [VL-LN Base Model](https://huggingface.co/InternRobotics/VL-LN-Bench-basemodel)
+
+After unzipping the base model, scene datasets, and trajectory data, put everything under VL-LN-Bench/ in the layout below.
+  ```bash
+  VL-LN-Bench/
+  ├── base_model/ 
+  │   └── iion/
+  ├── raw_data/ 
+  │   └── mp3d/
+  │       ├── scene_summary/
+  │       ├── train/ 
+  │       │   ├── train_ion.json.gz
+  │       │   └── train_iion.json.gz
+  │       └── val_unseen/ 
+  │           ├── val_unseen_ion.json.gz
+  │           └── val_unseen_iion.json.gz
+  ├── scene_datasets/
+  │   └── mp3d/
+  │       ├── 17DRP5sb8fy/
+  │       ├── 1LXtFkjw3qL/
+  │       ...
+  └── traj_data/
+      ├── mp3d_split1/
+      ├── mp3d_split2/
+      └── mp3d_split3/
+  ```
+
+### 2. Environment Setup
+Here we set up the Python environment for VL-LN Bench and InternVLA-N1. If you've already installed the InternNav Habitat environment, you can skip those steps and only run the commands related to VL-LN Bench.
+
+- Get Code
+  ```bash
+  git clone git@github.com:InternRobotics/VL-LN.git # code for data collection
+  git clone git@github.com:InternRobotics/InternNav.git # code for training and evaluation
+  ```
+
+- Create Conda Environment
+  ```bash
+  conda create -n vlln python=3.9 -y
+  conda activate vlln
+  ```
+
+- Install Dependencies
+  ```bash
+  conda install habitat-sim=0.2.4 withbullet headless -c conda-forge -c aihabitat
+  cd VL-LN
+  pip install -r requirements.txt
+  cd ../InternNav
+  pip install -e .
+  ```
+
+### 3. Guidance for Data Collection
+This step is optional. You can either use our collected data for policy training, or follow this step to collect your own training data.
+
+
+- Prerequisites:
+  - Get pointnav_weights.pth from [VLFM](https://github.com/bdaiinstitute/vlfm/tree/main/data)
+  - Arrange the Directory Structure Like This
+    ```bash
+    VL-LN
+    ├── dialog_generation/
+    ├── images/
+    ├── VL-LN-Bench/
+    │   ├── base_model/ 
+    │   ├── raw_data/ 
+    │   ├── scene_datasets/
+    │   ├── traj_data/
+    │   └── pointnav_weights.pth
+    ...
+    ```
+
+- Collect Trajectories
+  ```bash
+  # If having slurm
+  sbatch generate_frontiers_dialog.sh
+
+  # Or directly run
+  python generate_frontiers_dialog.py \
+      --task instance \
+      --vocabulary hm3d \
+      --scene_ids all \
+      --shortest_path_threshold 0.1 \
+      --target_detected_threshold 5 \
+      --episodes_file_path VL-LN-Bench/raw_data/mp3d/train/train_iion.json.gz \
+      --habitat_config_path dialog_generation/config/tasks/dialog_mp3d.yaml \
+      --baseline_config_path dialog_generation/config/expertiments/gen_videos.yaml \
+      --normal_category_path dialog_generation/normal_category.json \
+      --pointnav_policy_path VL-LN-Bench/pointnav_weights.pth\
+      --scene_summary_path VL-LN-Bench/raw_data/mp3d/scene_summary\
+      --output_dir <PATH_TO_YOUR_OUTPUT_DIR> \
+  ```
+
+### 4. Guidance for Training and Evaluation
+Here we show how to train your own model for the IIGN task and evaluate it on VL-LN Bench.
+
+- Prerequisites
+  ```bash
+  cd InternNav
+  # Link VL-LN Bench data into InternNav
+  mkdir projects && cd projects
+  ln -s /path/to/your/VL-LN-Bench ./VL-LN-Bench
+  ```
+  - Write your OpenAI API key to api_key.txt.
+  ```bash
+  # Your final repo structure may look like
+  InternNav
+  ├── assets/
+  ├── internnav/
+  │   ├── habitat_vlln_extensions
+  │   │   ├── simple_npc
+  │   │   │   ├── api_key.txt
+  │   ... ... ...
+  ...
+  ├── projects
+  │   ├── VL-LN-Bench/
+  │   │   ├── base_model/ 
+  │   │   ├── raw_data/ 
+  │   │   ├── scene_datasets/
+  │   │   ├── traj_data/
+  ... ...
+  ```
+
+- Start Training
+  ```bash
+  # Before running, please open this script and make sure 
+  # the "llm" path points to the correct checkpoint on your machine.
+  sbatch ./scripts/train/qwenvl_train/train_system2_vlln.sh
+  ```
+
+- Start Evaluation
+  ```bash
+  # If having slurm
+  sh ./scripts/eval/bash/srun_eval_dialog.sh
+
+  # Or directly run
+  python scripts/eval/eval.py \
+    --config scripts/eval/configs/habitat_dialog_cfg.py
+  ```

source/en/user_guide/internnav/projects/index.md

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+---
+myst:
+  html_meta:
+    "description lang=en": |
+      Documentation for users who wish to build sphinx sites with
+      pydata-sphinx-theme.
+---
+
+# Projects
+
+```{toctree}
+:caption: Projects
+:maxdepth: 2
+
+benchmark
+```