README.md

---
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/makeabilitylab/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{su2026capnav,
    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:2602.18424},
    year={2026}
}
```