Update README.md

README.md

@@ -1,3 +1,139 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+---
+
+# CapNav: Capability-Conditioned Indoor Navigation Benchmark
+
+CapNav is a benchmark for evaluating **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.
+
+---
+
+## Dataset Overview
+
+### 1. CapNav Benchmark Dataset
+
+- **File:** `capnav_v0_no_answer.parquet`
+- **Format:** Parquet
+- **Split:** `train`
+- **Rows:** 2,365 (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` (optional / null)**  
+  Ground-truth navigation feasibility label.  
+  This field is intentionally left empty in the current release and may be populated in future versions.
+
+---
+
+### 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.
+
+---
+
+## Relationship Between the Datasets
+
+Each row in the CapNav Benchmark Dataset references exactly one agent via `agent_name`.  
+The corresponding agent properties should be retrieved from the Agent Profiles Dataset
+before constructing prompts or evaluating navigation feasibility.
+
+This separation enables:
+- Modular benchmark design
+- Easy extension with new agent types
+- Stable navigation questions across different embodied agents
+
+---
+
+## 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 use this dataset in your research, please cite:
+