Update README.md

README.md

@@ -11,5 +11,151 @@ configs:
     path: agent_profiles.parquet
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-blank README
-will update when published
+# 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}
+}
+```