Update README.md

README.md

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----
-license: cc-by-4.0
-configs:
-- config_name: capnav_v0
-  data_files:
-  - split: train
-    path: capnav_v0_with_answer.parquet
-- config_name: agent_profiles
-  data_files:
-  - split: train
-    path: agent_profiles.parquet
----
-
-# CapNav: Benchmarking Vision Language Models on Capability-conditioned Indoor Navigation
-
-CapNav is a benchmark for Vision Language Models evaluating on **capability-conditioned navigation reasoning** in indoor environments.
-The benchmark focuses on determining whether an embodied agent with specific physical constraints and abilities
-can navigate from a start area to a target area within a complex indoor scene.
-
-This repository contains **two complementary datasets**:
-1. The main **CapNav Benchmark Dataset**, which provides navigation questions paired with scene graph nodes and agent identifiers.
-2. The **Agent Profiles Dataset**, which defines the physical dimensions and capabilities of each agent type.
-
----
-
-## Quickstart
-
-```python
-from datasets import load_dataset
-
-capnav = load_dataset("RichardC0216/CapNav", "capnav_v0", split="train")
-agents = load_dataset("RichardC0216/CapNav", "agent_profiles", split="train")
-
-print(len(capnav), capnav.column_names)
-print(len(agents), agents.column_names)
-```
-
-## Dataset Overview
-
-### 1. CapNav Benchmark Dataset (`capnav_v0`)
-- **File:** `capnav_v0_with_answer.parquet`
-- **Format:** Parquet
-- **Split:** `train`
-- **Rows:** 2,330 (question × agent combinations)
-This dataset contains natural language navigation questions grounded in indoor scenes.
-Each question is evaluated under a specific agent profile, enabling systematic analysis
-of how agent capabilities affect navigation feasibility.
-
-#### Data Fields
-Below one can find the description of each field in the dataset.
-- **`question_id` (str)**  
-  Unique identifier for each question–agent pair.
-- **`question` (str)**  
-  Natural language navigation question describing whether an agent can move from a start area to a goal area.
-- **`scene_id` (str)**  
-  Identifier of the indoor scene (e.g., `HM3D00000`, `MP3D00027`).
-- **`scene_type` (str)**  
-  High-level category of the scene (e.g., `home`).
-- **`scene_nodes` (list[dict])**  
-  List of scene graph nodes available in the environment.  
-  Each node contains:
-  - `node_id` (str): Unique identifier of the node  
-  - `name` (str): Human-readable node name (e.g., room or area label)
-- **`agent_name` (str)**  
-  Name of the agent under which the navigation question should be evaluated.
-  This field links to an entry in the Agent Profiles Dataset.
-- **`answer` ((bool))**  
-  Binary ground-truth navigability label (`True` / `False`) for the (`question`, `scene`, `agent`) triple.
-  > Note: `answer` only provides a **binary feasibility** signal. For scene graphs, route-level traversability,
-  > and detailed ground-truth rationale, please refer to the full annotations in `ground_truth/`(see below).
-
----
-
-### 2. Agent Profiles Dataset
-- **File:** `agent_profiles.parquet`
-- **Format:** Parquet
-- **Rows:** One per agent type
-This dataset defines the physical properties and functional capabilities of each agent
-used in the CapNav benchmark.
-
-#### Data Fields
-- **`agent_name` (str)**  
-  Unique identifier of the agent (e.g., `HUMAN`, `WHEELCHAIR`, `SWEEPER`).
-- **`body_shape` (str)**  
-  Abstract geometric representation of the agent’s body (e.g., `cylinder`, `box`).
-- **`body_height_m` (float)**  
-  Agent height in meters.
-- **`body_width_m` (float)**  
-  Agent width in meters.
-- **`body_depth_m` (float, optional)**  
-  Agent depth in meters.  
-  May be `null` for rotationally symmetric agents.
-- **`max_vertical_cross_height_m` (float)**  
-  Maximum vertical obstacle height the agent can cross.
-- **`can_go_up_or_down_stairs` (bool)**  
-  Indicates whether the agent is capable of traversing stairs.
-- **`can_operate_elevator` (bool)**  
-  Indicates whether the agent can operate and use elevators.
-- **`can_open_the_door` (bool)**  
-  Indicates whether the agent can open doors independently.
-- **`description` (str)**  
-  Natural language description summarizing the agent’s physical and functional characteristics.
----
-
-## Full Ground Truth Annotations (Graphs and Traversability)
-This repository also includes a `ground_truth/` directory that provides the
-**complete ground-truth annotations** used to derive the binary `answer` labels
-in the main CapNav benchmark.
-
-While the benchmark dataset exposes only a binary navigability outcome for each
-`(question, scene, agent)` triple, the annotations in `ground_truth/` contain
-the underlying **structural and traversability information** that supports
-more detailed inspection and analysis.
-
-Specifically, this directory includes:
-- **Scene graph annotations** (`ground_truth/graphs/`)  
-  Manually constructed graph representations for each indoor environment,
-  encoding nodes, connectivity, and true spatial structure.
-- **Agent-conditioned traversability annotations** (`ground_truth/traverse/`)  
-  Route-level annotations that specify whether and why a path is traversable
-  for a given agent, including agent-specific constraints and failure rationales.
-
-The binary `answer` field in the benchmark dataset is a **distilled signal**
-derived from these annotations. Researchers interested in path validity,
-failure cases, or the reasons behind infeasible navigation decisions should
-refer to the files in `ground_truth/`.
-
-Detailed descriptions of file formats and annotation semantics can be found in:
-- `ground_truth/README.md`
-
-The annotation pipeline, tooling, and quality control procedures are documented
-in the CapNav GitHub repository:
-- https://github.com/Ruiqi-Chen-0216/CapNav  
-  (see the annotation code and documentation)
-
----
-
-## Intended Use
-The CapNav benchmark is intended for:
-- Evaluating multimodal or vision-language models on embodied navigation reasoning
-- Studying how physical capabilities affect navigation feasibility
-- Benchmarking agent-aware reasoning and planning systems
-The dataset is **not** intended to provide low-level control signals or precise geometric navigation paths.
-
----
-
-## License
-- **Dataset:** Creative Commons Attribution 4.0 International (CC BY 4.0)
----
-
-## Citation
-
-If you find it useful for your research and applications, please cite our paper using this BibTeX:
-```bibtex
-@article{su2025capnav,
-    title={CapNav: Benchmarking Vision Language Models on Capability-conditioned Indoor Navigation},
-    author={Su, Xia  and Chen, Ruiqi and Liu, Benlin and Ma, Jingwei and Di, Zonglin and Krishna, Ranjay and Froehlich, Jon},
-    journal={arXiv preprint arXiv:xxxxxx},
-    year={2025}
-}
-```
+blank README
+will update when published