Datasets:

Rithvik762
/

VLNCE-EnvDrop

license: mit
task_categories:
  - video-text-to-text
  - robotics
language:
  - en
tags:
  - vision-language-navigation
  - VLN
  - navigation
  - embodied-ai
  - envdrop
size_categories:
  - 100K<n<1M

VLNCE-EnvDrop

Synthetic Vision-Language Navigation (VLN) data-augmentation set, derived from the EnvDrop augmentation used in VLN-CE / NaVILA-style training. Each of the 146,304 samples pairs a short first-person navigation video with the natural-language instruction the agent was following and the discrete action sequence it executed.

This dataset provides the visual + motion supervision for training a GRU-augmented Qwen3-VL navigation model: the language conditions the backbone, while the per-step motion sequence feeds a GRU whose output is projected into the LLM embedding space.

File	Size	What it is
`envdrop_videos_00.tar` … `envdrop_videos_14.tar`	~270 GB	The raw first-person navigation videos, one `<video_id>.mp4` per sample, sharded into 15 tarballs.
`envdrop_motion.json`	392 MB	Primary training annotation. One record per sample: instruction, decoded frame paths, and the per-step action (`motion`) sequence.
`annotations.json`	22.5 MB	Lightweight `video_id → instruction` index (powers the dataset preview). A subset of the info in `envdrop_motion.json`.

Record schema — `envdrop_motion.json`

{
  "video_id": "34300",
  "q": "Walk forward and stop at the end of the aisle.",
  "frames": ["34300/frame_0.jpg", "34300/frame_1.jpg", "..."],
  "motion": [3, 3, 1, 1, 3, 1, 1, 2, 2, 1, "..."]
}

video_id — key into the tarballs (<video_id>.mp4).
q — the natural-language navigation instruction.
frames — decoded frame paths for the clip (frames are extracted from the corresponding .mp4 at load time; they are not stored separately).
motion — the discrete action taken at each step (small action vocabulary, e.g. forward / turn-left / turn-right / stop). This is the GRU input.

Layout

VLNCE-EnvDrop/
├── envdrop_videos_00.tar        # <video_id>.mp4 clips
│   ...                          # (15 shards, ~270 GB total)
├── envdrop_videos_14.tar
├── envdrop_motion.json          # primary training annotation (146,304 records)
└── annotations.json             # video_id -> instruction index / preview

Usage

from huggingface_hub import snapshot_download

# annotations only (small)
snapshot_download("Rithvik762/VLNCE-EnvDrop", repo_type="dataset",
                  allow_patterns=["*.json"])

# full dataset incl. video tars (~270 GB)
snapshot_download("Rithvik762/VLNCE-EnvDrop", repo_type="dataset")

After download, extract the shards (e.g. for f in envdrop_videos_*.tar; do tar xf "$f"; done). Videos and envdrop_motion.json must be kept together — the JSON references video_ids that live inside the tarballs.

License

Released under the MIT license.

VLNCE-EnvDrop

Contents

Record schema — envdrop_motion.json

Layout

Usage

License

Record schema — `envdrop_motion.json`