Datasets:

psiang
/

ReCoNavBench

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 · Data terms

Citation

@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 and HM3DSem when using the underlying scenes.

Download

Accept the access terms on this dataset's page, then download:

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.

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:

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. The relevant upstream packages are:

Download	Scene files
hm3d-val-habitat-v0.2.tar	Habitat `.basis.glb` geometry
hm3d-val-glb-v0.2.tar	Original JPEG-textured `.glb` for rendering

ReCoNav uses these nine scene directories, also listed in benchmark/manifest.json:

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:

export RECONAV_HM3D_ROOT=/absolute/path/to/hm3d/val

For example:

$RECONAV_HM3D_ROOT/
└── 00824-Dd4bFSTQ8gi/
    ├── Dd4bFSTQ8gi.basis.glb
    └── Dd4bFSTQ8gi.glb

Use the same directory pattern for the other eight scenes. See the baseline manual for the separate Habitat navmesh resource.

Terms

HM3D/HM3DSem-derived data is subject to the upstream academic, non-commercial terms in DATA_TERMS.md. The code repository has an Apache-2.0 license; this dataset retains its data terms.

ReCoNav Benchmark

Citation

Download

Contents

Use

Original scene meshes

Terms