The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 84, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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.
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:
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.
- Downloads last month
- 30