Datasets:
Languages:
English
Size:
n<1K
Tags:
robotics
drone-navigation
vision-language-navigation
open-vocabulary-detection
embodied-ai
habitat-sim
License:
Initial reviewer sample: 50 waypoints from hssd-102343992 (~500MB) + dataset card
Browse files- README.md +123 -0
- hssd-102343992_wp0000.npz +3 -0
README.md
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---
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license: cc-by-nc-4.0
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tags:
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- robotics
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- drone-navigation
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- vision-language-navigation
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- open-vocabulary-detection
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- embodied-ai
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- habitat-sim
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- benchmark
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- preview-sample
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task_categories:
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- object-detection
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- depth-estimation
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- robotics
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size_categories:
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- n<1K
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pretty_name: "Yonder (NeurIPS reviewer sample)"
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language:
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- en
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---
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# Yonder Sample — NeurIPS 2026 Reviewer Preview
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This is a **~500 MB sample** of the [Yonder](https://huggingface.co/datasets/astralhf/yonder)
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drone navigation dataset, intended for fast inspection by NeurIPS reviewers and others
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who want to verify the data format and content before downloading the full 3.8 TB release.
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## What's included
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- **One HSSD scene:** `hssd-102343992`
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- **50 consecutive waypoint NPZs** (`wp0000` through `wp0049`)
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- **All 12 yaw orientations** per waypoint
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- **All sensor modalities** present in the full dataset:
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stereo RGB (left/right), forward depth, landing-camera, up-IR, down-IR,
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360° LiDAR, position, orientation, IMU
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- ~50 × 10 MB ≈ **500 MB** total
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## What's NOT in this sample
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- **Semantic segmentation** — this particular scene does not have semantic annotations.
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The full dataset includes semantics for 92 of 251 scenes; download the main repo and
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filter by `manifest.json` `has_semantics: true` to obtain a semantics-bearing sample.
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- **Multiple scenes** — by design. This sample is a single-scene slice. The full
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release spans 251 scenes (167 HSSD + 84 ReplicaCAD).
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## Quick start
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```python
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from huggingface_hub import snapshot_download
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import numpy as np
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local = snapshot_download(repo_id="astralhf/yonder-sample", repo_type="dataset")
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# Inspect the first waypoint
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data = np.load(f"{local}/hssd-102343992_wp0000.npz")
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print(sorted(data.keys())[:10])
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# ['down_ir', 'lidar360', 'orientation', 'position', 'up_ir',
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# 'yaw000_forward_depth', 'yaw000_landing_cam', 'yaw000_left_rgb',
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# 'yaw000_right_rgb', 'yaw001_forward_depth']
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# Shapes
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print("left_rgb yaw000:", data["yaw000_left_rgb"].shape, data["yaw000_left_rgb"].dtype)
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# left_rgb yaw000: (480, 640, 3) uint8
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print("forward_depth yaw000:", data["yaw000_forward_depth"].shape, data["yaw000_forward_depth"].dtype)
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# forward_depth yaw000: (480, 640) float16
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print("lidar360:", data["lidar360"].shape, data["lidar360"].dtype)
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# lidar360: (1024, 16) float32
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print("position:", data["position"])
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# position: (3,) float32 — Habitat-Sim world frame
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```
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## Visualizing a frame
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```python
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import numpy as np
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import matplotlib.pyplot as plt
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data = np.load("hssd-102343992_wp0000.npz")
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fig, axes = plt.subplots(1, 3, figsize=(15, 5))
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axes[0].imshow(data["yaw000_left_rgb"]); axes[0].set_title("Left RGB")
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axes[1].imshow(data["yaw000_right_rgb"]); axes[1].set_title("Right RGB")
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axes[2].imshow(data["yaw000_forward_depth"], cmap="plasma")
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axes[2].set_title("Forward depth (m)")
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for a in axes: a.axis("off")
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plt.tight_layout(); plt.savefig("yonder_sample.png", dpi=150)
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```
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## Going to the full dataset
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```python
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# Single scene from the full repo (~25 GB)
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snapshot_download(
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repo_id="astralhf/yonder",
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repo_type="dataset",
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allow_patterns="indoor/drone-data/augmented/hssd-102343992/*.npz",
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)
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# Just the manifests (a few MB) to plan a custom download
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snapshot_download(
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repo_id="astralhf/yonder",
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repo_type="dataset",
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allow_patterns="indoor/drone-data/augmented/*/manifest.json",
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)
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```
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## License
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CC-BY-NC-4.0 (inheriting HSSD's NonCommercial restriction). See the
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[main dataset card](https://huggingface.co/datasets/astralhf/yonder) for full
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licensing and Responsible AI considerations.
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## Citation
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```bibtex
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@inproceedings{anonymous2026yonder,
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title = {Yonder: A 6.1M-Frame Drone Navigation Dataset and the Cross-Simulator Generalization Gap},
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author = {Anonymous Author(s)},
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booktitle = {NeurIPS Datasets and Benchmarks Track},
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year = {2026},
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note = {Anonymized for double-blind review.}
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}
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```
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hssd-102343992_wp0000.npz
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version https://git-lfs.github.com/spec/v1
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oid sha256:80c2db58196c8d0e6de031a662b1876bfb60956b8f120db020f27d97ac54a5c8
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size 11849675
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