| --- |
| pretty_name: ReCoNav Benchmark |
| language: |
| - en |
| license: other |
| license_name: hm3d-hm3dsem-academic-use |
| license_link: https://huggingface.co/datasets/psiang/ReCoNavBench/blob/main/DATA_TERMS.md |
| size_categories: |
| - n<1K |
| tags: |
| - vision-and-language-navigation |
| - embodied-ai |
| - navigation |
| - benchmark |
| - hm3d |
| extra_gated_prompt: "Read DATA_TERMS.md and the Matterport Academic Use Agreement at https://matterport.com/legal/matterport-end-user-license-agreement-academic-use-model-data before requesting access." |
| extra_gated_fields: |
| Name: text |
| Institution: text |
| I accept the Matterport Academic Use Agreement and the dataset terms: checkbox |
| --- |
| |
| # ReCoNav Benchmark |
|
|
| **192 constrained navigation tasks across nine HM3DSem scenes**, with scene ASGs, physical navigation geometry and evaluator annotations. Tasks cover goals, ordered visits, avoidance, counts, preferences and combinations of these requirements. All tasks are evaluation tasks. |
|
|
| [Code and running instructions](https://github.com/psiang/ReCoNav) · [Data terms](DATA_TERMS.md) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{peng2026reconav, |
| title = {{ReCoNav}: Reliable Constrained Long-Horizon Vision-and-Language Navigation via a Determination Engine}, |
| author = {Peng, Sixiang and Yu, Zehuan and Liu, Wenyi and Xie, Zhensheng and Zhang, Charles and Jiang, Chenhan}, |
| year = {2026}, |
| url = {https://github.com/psiang/ReCoNav} |
| } |
| ``` |
|
|
| Also cite [HM3D](https://aihabitat.org/datasets/hm3d/) and [HM3DSem](https://aihabitat.org/datasets/hm3d-semantics/) when using the underlying scenes. |
|
|
| ## Download |
|
|
| Accept the access terms on [this dataset's page](https://huggingface.co/datasets/psiang/ReCoNavBench), then download: |
|
|
| ```bash |
| python -m pip install huggingface_hub |
| hf auth login |
| export RECONAV_BENCHMARK_REPO=psiang/ReCoNavBench |
| export RECONAV_DATA_ROOT=/absolute/path/to/data |
| hf download "$RECONAV_BENCHMARK_REPO" reconav-benchmark-v1.0.0.tar.gz \ |
| --repo-type dataset --revision v1.0.0 --local-dir downloads |
| mkdir -p "$RECONAV_DATA_ROOT" |
| tar -xzf downloads/reconav-benchmark-v1.0.0.tar.gz -C "$RECONAV_DATA_ROOT" |
| ``` |
|
|
| Version: **1.0.0**. The archive extracts one `benchmark/` directory. Data can be stored anywhere, independently of the code checkout. |
|
|
| ## Contents |
|
|
| | Path under `benchmark/` | Contents | |
| |---|---| |
| | `tasks/hm3dsem/<scene>/<task>.json` | Instruction/start, task category, gold symbolic requirements, target metadata and reference route | |
| | `scenes/hm3dsem/<scene>/asg/` | Entity, geometry and topology JSON files | |
| | `scenes/hm3dsem/<scene>/navigation/graph.json.gz` | Physical free-space geometry and connectivity for route planning and evaluation | |
| | `splits/` | `all.json` (192 tasks) and `hard44.json` (44 tasks) | |
| | `vocabularies/` | Object and region category mappings | |
| | `manifest.json` | Version, scene IDs, task counts and coordinate conventions | |
|
|
| Hard44 contains 44 hard-constraint-resolving tasks supported by every compared method. Each task's `uniqueness_proof.identity_class` records whether target identity is resolved by a local qualifier, direct reference, hard constraint or preference. |
|
|
| Prediction inputs are the instruction, start and scene information. Gold target IDs, symbolic contracts and route witnesses are evaluator annotations. Route points use Habitat x/y/z, with y vertical; task `robot_position` uses x/y horizontally and `floor_height` vertically. ASG footprints use x/z. |
|
|
| ## Use |
|
|
| Install the code repository, then inspect the benchmark: |
|
|
| ```bash |
| reconav benchmark |
| ``` |
|
|
| Follow the repository README for model setup and a one-task ReCoNav run. Choose `--split all`, `--split hard44` or `--task-ids ID [ID ...]` independently of the selected method. A new method can use the task instructions/starts and ASGs with the repository's common evaluator. |
|
|
| Follow the code repository for method-specific environments, checkpoints and resources. |
|
|
| ## Original scene meshes |
|
|
| ReCoNav and its ablations use the packaged ASGs and physical graphs. Simulator and image-rendering methods also need HM3D v0.2 validation scene files. Sign in to Matterport and follow the [official download/access instructions](https://github.com/matterport/habitat-matterport-3dresearch). The relevant upstream packages are: |
|
|
| | Download | Scene files | |
| |---|---| |
| | [hm3d-val-habitat-v0.2.tar](https://api.matterport.com/resources/habitat/hm3d-val-habitat-v0.2.tar) | Habitat `.basis.glb` geometry | |
| | [hm3d-val-glb-v0.2.tar](https://api.matterport.com/resources/habitat/hm3d-val-glb-v0.2.tar) | Original JPEG-textured `.glb` for rendering | |
|
|
| ReCoNav uses these nine scene directories, also listed in `benchmark/manifest.json`: |
|
|
| ```text |
| 00824-Dd4bFSTQ8gi |
| 00829-QaLdnwvtxbs |
| 00843-DYehNKdT76V |
| 00848-ziup5kvtCCR |
| 00849-a8BtkwhxdRV |
| 00853-5cdEh9F2hJL |
| 00861-GLAQ4DNUx5U |
| 00873-bxsVRursffK |
| 00891-cvZr5TUy5C5 |
| ``` |
|
|
| Keep each selected scene's files together and point `RECONAV_HM3D_ROOT` to the `val/` directory: |
|
|
| ```bash |
| export RECONAV_HM3D_ROOT=/absolute/path/to/hm3d/val |
| ``` |
|
|
| For example: |
|
|
| ```text |
| $RECONAV_HM3D_ROOT/ |
| └── 00824-Dd4bFSTQ8gi/ |
| ├── Dd4bFSTQ8gi.basis.glb |
| └── Dd4bFSTQ8gi.glb |
| ``` |
|
|
| Use the same directory pattern for the other eight scenes. See the [baseline manual](https://github.com/psiang/ReCoNav/blob/main/docs/baselines.md#habitat-navigation-resources) for the separate Habitat navmesh resource. |
|
|
| ## Terms |
|
|
| HM3D/HM3DSem-derived data is subject to the upstream academic, non-commercial terms in [DATA_TERMS.md](DATA_TERMS.md). The code repository has an Apache-2.0 license; this dataset retains its data terms. |
|
|
