Update README.md

README.md

@@ -1,10 +1,19 @@
 ---
 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: Capability-Conditioned Indoor Navigation Benchmark
 
-CapNav is a benchmark for evaluating **capability-conditioned navigation reasoning** in indoor environments.  
+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.
 
@@ -14,23 +23,31 @@ This repository contains **two complementary datasets**:
 
 ---
 
-## Dataset Overview
+## 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")
 
-### 1. CapNav Benchmark Dataset
+print(len(capnav), capnav.column_names)
+print(len(agents), agents.column_names)
+```
+
+## Dataset Overview
 
-- **File:** `capnav_v0_no_answer.parquet`
+### 1. CapNav Benchmark Dataset (`capnav_v0`)
+- **File:** `capnav_v0_with_answer.parquet`
 - **Format:** Parquet
 - **Split:** `train`
-- **Rows:** 2,365 (question × agent combinations)
-
+- **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)**  
@@ -47,18 +64,17 @@ Below one can find the description of each field in the dataset.
 - **`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.
+- **`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.
 
@@ -86,37 +102,60 @@ used in the CapNav benchmark.
   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
+## 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 use this dataset in your research, please cite:
-
+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}
+}
+```