Update dataset card with recomputed statistics

README.md

@@ -1,13 +1,21 @@
 ---
 license: apache-2.0
-task_categories:
-- robotics
 tags:
-- LeRobot
 - robotics
+- lerobot
 - manipulation
 - imitation-learning
-- NIAT
+- vision-language-action
+- embodied-ai
+- teleoperation
+- so_follower
+task_categories:
+- robotics
+language:
+- en
+size_categories:
+- 100K<n<1M
+pretty_name: NIAT10
 configs:
 - config_name: Eraser_drawer
   data_files:
@@ -107,76 +115,191 @@ configs:
     path: Cube_pick_place/data/**/*.parquet
 ---
 
-# NIAT10
+# NIAT10 — A Tabletop Manipulation Dataset Collection
+
+24 teleoperated robot manipulation datasets recorded at NIAT, consolidated
+into a single repository for imitation-learning and vision-language-action research.
+
+<!-- TODO: banner image URL -->
+
+## Overview
+
+NIAT10 gathers every manipulation dataset recorded by the NIAT Physical AI group into
+one place, each one preserved in its original [LeRobot](https://github.com/huggingface/lerobot)
+layout inside its own top-level folder. Rather than a single merged dataset, it is a
+**curated collection** — you can train on one folder, a task family, or all of them.
+
+The collection covers five task families on a shared tabletop setup: pick and place,
+sorting, stacking, object reorientation, and non-prehensile pushing. Every episode was
+teleoperated on an **SO-101 (leader–follower pair)** through a leader arm and recorded from an
+overhead and a wrist camera at 640 × 480 / 30 fps.
+
+<!-- TODO: 2-3 sentences on why this collection exists — the research question,
+     the course/lab it came out of, what you intend to train on it. -->
+
+## Dataset Statistics
+
+| Metric | Value |
+|---|---|
+| **Total datasets** | 24 |
+| **Total episodes** | 2,038 |
+| **Total frames** | 975,654 |
+| **Total duration** | 09:02:02 (9.03 hours) |
+| **Unique recorded duration** | 08:10:46 (8.18 hours) |
+| **Unique episodes** | 1,738 |
+| **Task families** | 5 |
+| **Robot embodiments** | 1 |
+| **Average duration/dataset** | 0.38 hours |
+| **Approved datasets** | 19 of 24 |
+
+Durations are computed as `total_frames / fps` from each dataset's `meta/info.json`,
+so they reflect the current contents of each repo after episode pruning.
+
+**Unique vs total.** 1 dataset(s) re-annotate episodes that already appear
+elsewhere in the collection rather than adding new recordings — see
+[Language variants](#language-variants). The unique figures exclude them, and are the
+honest number to quote for how much robot time this collection represents.
+
+## Task Distribution
+
+| Task family | Datasets | Episodes | Duration | % of data |
+|---|---|---|---|---|
+| Pick and place | 14 | 1210 | 05:01:17 | 55.6% |
+| Sorting | 4 | 374 | 02:08:26 | 23.7% |
+| Non-prehensile push | 2 | 244 | 00:49:07 | 9.1% |
+| Orientation | 2 | 98 | 00:31:52 | 5.9% |
+| Stacking | 2 | 112 | 00:31:20 | 5.8% |
+
+<!-- TODO: task montage image URL -->
+
+## Robot Types
+
+| Robot type | Datasets | % |
+|---|---|---|
+| so_follower | 24 | 100.0% |
+
+Recorded with LeRobot v3.0 (24 datasets). Frame rates present: 30 fps (24).
+
+## Contributors
 
-A consolidated collection of 24 teleoperated robot manipulation datasets recorded at
-NIAT, gathered into a single repository. Each source dataset keeps its original
-[LeRobot](https://github.com/huggingface/lerobot) layout (`meta/`, `data/`, `videos/`)
-inside its own top-level folder.
+<!-- TODO: add named contributors to CONTRIBUTORS in niat10_card.py -->
 
-Roughly **9.3 hours** of recorded interaction across pick-and-place,
-sorting, stacking, orientation and non-prehensile pushing tasks.
+Recorded and published under the accounts below.
 
+| Account | Datasets | % |
+|---|---|---|
+| [NIATphysicalAI](https://huggingface.co/NIATphysicalAI) | 19 | 79.2% |
+| [Bradx86](https://huggingface.co/Bradx86) | 5 | 20.8% |
+
+Please credit the individual contributors above, not only the publishing accounts,
+when using this collection.
 ## Contents
 
-| # | Folder | Task | Duration | Episodes | Review | Source |
-|---|--------|------|----------|----------|--------|--------|
-| 1 | `Eraser_drawer` | Pick n place | 00:44:24 | 100 | fix required | [NIATphysicalAI/Eraser_drawer](https://huggingface.co/datasets/NIATphysicalAI/Eraser_drawer) |
-| 2 | `Battery_sort` | Sorting | 00:55:06 | 106 | Approved | [NIATphysicalAI/Battery_sort](https://huggingface.co/datasets/NIATphysicalAI/Battery_sort) |
-| 3 | `Battery_sort_v1` | Sorting | 00:03:12 | 7 | Approved | [NIATphysicalAI/Battery_sort_v1](https://huggingface.co/datasets/NIATphysicalAI/Battery_sort_v1) |
-| 4 | `drawer_cube_screwdriver` | Pick n place | 00:30:30 | 101 | Approved | [NIATphysicalAI/drawer_cube_screwdriver](https://huggingface.co/datasets/NIATphysicalAI/drawer_cube_screwdriver) |
-| 5 | `Drawer_screwdriver_v2` | Pick n place | 00:17:06 | 51 | Approved | [NIATphysicalAI/Drawer_screwdriver_v2](https://huggingface.co/datasets/NIATphysicalAI/Drawer_screwdriver_v2) |
-| 6 | `Drawer_screwdriver` | Pick n place | 00:12:54 | 34 | Approved | [NIATphysicalAI/Drawer_screwdriver](https://huggingface.co/datasets/NIATphysicalAI/Drawer_screwdriver) |
-| 7 | `screwdriver_box` | Pick n place | 00:18:12 | 47 | fix required | [NIATphysicalAI/screwdriver_box](https://huggingface.co/datasets/NIATphysicalAI/screwdriver_box) |
-| 8 | `screwdriver_box_v1` | Pick n place | 00:07:42 | 20 | fix required | [NIATphysicalAI/screwdriver_box_v1](https://huggingface.co/datasets/NIATphysicalAI/screwdriver_box_v1) |
-| 9 | `screwdriver_box_v0` | Pick n place | 00:04:12 | 10 | Approved | [NIATphysicalAI/screwdriver_box_v0](https://huggingface.co/datasets/NIATphysicalAI/screwdriver_box_v0) |
-| 10 | `stack_cubes` | Stacking | 00:17:06 | 58 | Re-record | [NIATphysicalAI/stack_cubes](https://huggingface.co/datasets/NIATphysicalAI/stack_cubes) |
-| 11 | `eraser` | Pick n place | 00:15:30 | 60 | Approved | [NIATphysicalAI/eraser](https://huggingface.co/datasets/NIATphysicalAI/eraser) |
-| 12 | `cube` | Stacking | 00:14:12 | 54 | fix required | [NIATphysicalAI/cube](https://huggingface.co/datasets/NIATphysicalAI/cube) |
-| 13 | `stapler` | Pick n place | 00:17:54 | 37 | Approved | [NIATphysicalAI/stapler](https://huggingface.co/datasets/NIATphysicalAI/stapler) |
-| 14 | `bottle` | Orientation/reposition | 00:16:24 | 48 | Approved | [NIATphysicalAI/bottle](https://huggingface.co/datasets/NIATphysicalAI/bottle) |
-| 15 | `penholder` | Sorting | 00:26:12 | 92 | Approved | [NIATphysicalAI/penholder](https://huggingface.co/datasets/NIATphysicalAI/penholder) |
-| 16 | `sort_biodegradable` | Sorting | 00:43:42 | 169 | Approved | [NIATphysicalAI/sort_biodegradable](https://huggingface.co/datasets/NIATphysicalAI/sort_biodegradable) |
-| 17 | `pick_place_cubes_para` | Sorting | 00:51:18 | 300 | Approved | [NIATphysicalAI/pick_place_cubes_para](https://huggingface.co/datasets/NIATphysicalAI/pick_place_cubes_para) |
-| 18 | `pick_place_cubes` | Sorting | 00:51:18 | 300 | Approved | [NIATphysicalAI/pick_place_cubes](https://huggingface.co/datasets/NIATphysicalAI/pick_place_cubes) |
-| 19 | `chocolate_pick_place` | Pick n place | 00:16:12 | 50 | Approved | [NIATphysicalAI/chocolate_pick_place](https://huggingface.co/datasets/NIATphysicalAI/chocolate_pick_place) |
-| 20 | `pick_place` | Pick n place | 00:10:18 | 50 | Approved | [Bradx86/pick-place](https://huggingface.co/datasets/Bradx86/pick-place) |
-| 21 | `push_t_v2` | Contact(no grasp) | 00:43:54 | 194 | Approved | [Bradx86/push_t_v2](https://huggingface.co/datasets/Bradx86/push_t_v2) |
-| 22 | `t_push` | Contact(no grasp) | 00:05:12 | 50 | Approved | [Bradx86/t_push](https://huggingface.co/datasets/Bradx86/t_push) |
-| 23 | `Bottle_orient` | Pick n place | 00:17:54 | 50 | Approved | [Bradx86/bottle-test](https://huggingface.co/datasets/Bradx86/bottle-test) |
-| 24 | `Cube_pick_place` | Pick n place | 00:16:00 | 50 | Approved | [Bradx86/record-test](https://huggingface.co/datasets/Bradx86/record-test) |
-
-## Layout
+| # | Folder | Task | Episodes | Frames | Duration | FPS | Review | Source |
+|---|---|---|---|---|---|---|---|---|
+| 1 | `Eraser_drawer` | Pick and place | 100 | 79,890 | 00:44:23 | 30 | fix required | [Eraser_drawer](https://huggingface.co/datasets/NIATphysicalAI/Eraser_drawer) |
+| 2 | `Battery_sort` | Sorting | 106 | 93,878 | 00:52:09 | 30 | Approved | [Battery_sort](https://huggingface.co/datasets/NIATphysicalAI/Battery_sort) |
+| 3 | `Battery_sort_v1` | Sorting | 7 | 5,681 | 00:03:09 | 30 | Approved | [Battery_sort_v1](https://huggingface.co/datasets/NIATphysicalAI/Battery_sort_v1) |
+| 4 | `drawer_cube_screwdriver` | Pick and place | 101 | 54,895 | 00:30:30 | 30 | Approved | [drawer_cube_screwdriver](https://huggingface.co/datasets/NIATphysicalAI/drawer_cube_screwdriver) |
+| 5 | `Drawer_screwdriver_v2` | Pick and place | 51 | 30,709 | 00:17:04 | 30 | Approved | [Drawer_screwdriver_v2](https://huggingface.co/datasets/NIATphysicalAI/Drawer_screwdriver_v2) |
+| 6 | `Drawer_screwdriver` | Pick and place | 34 | 23,221 | 00:12:54 | 30 | Approved | [Drawer_screwdriver](https://huggingface.co/datasets/NIATphysicalAI/Drawer_screwdriver) |
+| 7 | `screwdriver_box` | Pick and place | 47 | 27,536 | 00:15:18 | 30 | fix required | [screwdriver_box](https://huggingface.co/datasets/NIATphysicalAI/screwdriver_box) |
+| 8 | `screwdriver_box_v1` | Pick and place | 20 | 13,829 | 00:07:41 | 30 | fix required | [screwdriver_box_v1](https://huggingface.co/datasets/NIATphysicalAI/screwdriver_box_v1) |
+| 9 | `screwdriver_box_v0` | Pick and place | 10 | 7,498 | 00:04:10 | 30 | Approved | [screwdriver_box_v0](https://huggingface.co/datasets/NIATphysicalAI/screwdriver_box_v0) |
+| 10 | `stack_cubes` | Stacking | 58 | 30,806 | 00:17:07 | 30 | Re-record | [stack_cubes](https://huggingface.co/datasets/NIATphysicalAI/stack_cubes) |
+| 11 | `eraser` | Pick and place | 60 | 26,893 | 00:14:56 | 30 | Approved | [eraser](https://huggingface.co/datasets/NIATphysicalAI/eraser) |
+| 12 | `cube` | Stacking | 54 | 25,595 | 00:14:13 | 30 | fix required | [cube](https://huggingface.co/datasets/NIATphysicalAI/cube) |
+| 13 | `stapler` | Pick and place | 37 | 16,750 | 00:09:18 | 30 | Approved | [stapler](https://huggingface.co/datasets/NIATphysicalAI/stapler) |
+| 14 | `bottle` | Orientation | 48 | 25,162 | 00:13:59 | 30 | Approved | [bottle](https://huggingface.co/datasets/NIATphysicalAI/bottle) |
+| 15 | `penholder` | Sorting | 92 | 43,990 | 00:24:26 | 30 | Approved | [penholder](https://huggingface.co/datasets/NIATphysicalAI/penholder) |
+| 16 | `sort_biodegradable` | Sorting | 169 | 87,635 | 00:48:41 | 30 | Approved | [sort_biodegradable](https://huggingface.co/datasets/NIATphysicalAI/sort_biodegradable) |
+| 17 | `pick_place_cubes_para` | Pick and place | 300 | 92,287 | 00:51:16 | 30 | Approved | [pick_place_cubes_para](https://huggingface.co/datasets/NIATphysicalAI/pick_place_cubes_para) |
+| 18 | `pick_place_cubes` | Pick and place | 300 | 92,287 | 00:51:16 | 30 | Approved | [pick_place_cubes](https://huggingface.co/datasets/NIATphysicalAI/pick_place_cubes) |
+| 19 | `chocolate_pick_place` | Pick and place | 50 | 29,178 | 00:16:13 | 30 | Approved | [chocolate_pick_place](https://huggingface.co/datasets/NIATphysicalAI/chocolate_pick_place) |
+| 20 | `pick_place` | Pick and place | 50 | 18,559 | 00:10:19 | 30 | Approved | [pick-place](https://huggingface.co/datasets/Bradx86/pick-place) |
+| 21 | `push_t_v2` | Non-prehensile push | 194 | 79,027 | 00:43:54 | 30 | Approved | [push_t_v2](https://huggingface.co/datasets/Bradx86/push_t_v2) |
+| 22 | `t_push` | Non-prehensile push | 50 | 9,388 | 00:05:13 | 30 | Approved | [t_push](https://huggingface.co/datasets/Bradx86/t_push) |
+| 23 | `Bottle_orient` | Orientation | 50 | 32,196 | 00:17:53 | 30 | Approved | [bottle-test](https://huggingface.co/datasets/Bradx86/bottle-test) |
+| 24 | `Cube_pick_place` | Pick and place | 50 | 28,764 | 00:15:59 | 30 | Approved | [record-test](https://huggingface.co/datasets/Bradx86/record-test) |
+
+## Repository Structure
 
 ```
 NIAT10/
 ├── <dataset_name>/
-│   ├── meta/          # info.json, episodes.jsonl, tasks.jsonl, stats
-│   ├── data/          # parquet chunks of states / actions
-│   └── videos/        # per-camera mp4 recordings
+│   ├── meta/
+│   │   ├── info.json          # fps, robot type, feature schema, totals
+│   │   ├── episodes.jsonl     # per-episode index and lengths
+│   │   ├── tasks.jsonl        # natural-language task strings
+│   │   └── stats.json         # per-feature normalisation statistics
+│   ├── data/
+│   │   └── chunk-000/
+│   │       └── episode_*.parquet   # states, actions, timestamps
+│   └── videos/
+│       └── chunk-000/
+│           └── observation.images.<cam>/episode_*.mp4
 └── ...
 ```
 
-## Loading a single dataset
+## Usage
+
+### Authenticate
+
+```bash
+hf auth login
+# or: export HF_TOKEN=your_token_here
+```
+
+### Download a single dataset
+
+Each folder is self-contained, so pull only what you need:
+
+```bash
+hf download NIATphysicalAI/NIAT10 \
+    --repo-type=dataset \
+    --include "Battery_sort/*" \
+    --local-dir ./NIAT10
+```
+
+### Download everything
 
-Each folder is a self-contained LeRobot dataset. Point `root` at the folder after
-downloading it:
+```bash
+hf download NIATphysicalAI/NIAT10 --repo-type=dataset --local-dir ./NIAT10
+```
+
+### Load with LeRobot
 
 ```python
-from huggingface_hub import snapshot_download
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+ds = LeRobotDataset(repo_id="NIATphysicalAI/NIAT10", root="./NIAT10/Battery_sort")
+
+print(f"Episodes: {ds.num_episodes}")
+print(f"Frames:   {ds.num_frames}")
+print(f"Task:     {ds.meta.tasks}")
+
+sample = ds[0]
+print(sample.keys())
+```
+
+### Browse the collection
 
-path = snapshot_download(
-    repo_id="NIATphysicalAI/NIAT10",
-    repo_type="dataset",
-    allow_patterns="Battery_sort/*",
-    local_dir="NIAT10",
-)
+```python
+from pathlib import Path
+import json
 
-ds = LeRobotDataset(repo_id="NIATphysicalAI/NIAT10", root="NIAT10/Battery_sort")
+for folder in sorted(Path("./NIAT10").iterdir()):
+    info_path = folder / "meta" / "info.json"
+    if info_path.exists():
+        info = json.loads(info_path.read_text())
+        hours = info["total_frames"] / info["fps"] / 3600
+        print(f"{folder.name:<26} {info['total_episodes']:>4} episodes  {hours:.2f} h")
 ```
 
-The tabular part of each dataset is also registered as a named config:
+### Load the tabular data without video
+
+Each folder is registered as a named config, so the state/action streams can be read
+directly with `datasets`:
 
 ```python
 from datasets import load_dataset
@@ -184,7 +307,181 @@ from datasets import load_dataset
 ds = load_dataset("NIATphysicalAI/NIAT10", "Battery_sort", split="train")
 ```
 
-## Review status
+## Training
+
+<!-- TODO: replace with the exact command you actually ran, once you have one. -->
+
+```bash
+lerobot-train \
+    --policy.type=act \
+    --dataset.repo_id=NIATphysicalAI/NIAT10 \
+    --dataset.root=./NIAT10/Battery_sort \
+    --output_dir=./outputs/act_battery_sort \
+    --batch_size=8 \
+    --steps=100000
+```
+
+To train across several folders, pass a comma-separated list of roots or build a
+`MultiLeRobotDataset` over the folders you want.
+
+## Known Issues and Caveats
+
+Community-recorded teleoperation data is never uniform. Things to check before training:
+
+- **Review status.** 5 of 24 datasets are flagged below. They are
+  included for completeness; prefer the approved subset for headline results.
+- **Varying episode counts.** Some datasets are short (a few minutes) and will be
+  heavily under-represented in a naive concatenation. Consider weighted sampling.
+- **Camera configurations differ** between datasets. Check
+  `observation.images.*` in each `meta/info.json` before batching across folders.
+- **Frame rates.** 30 fps (24) — resample or filter if your policy assumes a fixed rate.
+- **Episode indices are per-folder.** They are not globally unique across the collection.
+- **Not a single merged dataset.** Loading NIAT10 as one `LeRobotDataset` will not work;
+  point `root` at an individual folder.
+
+### Flagged datasets
+
+| Folder | Status | Note |
+|---|---|---|
+| `Eraser_drawer` | fix required | <!-- TODO: what needs fixing --> |
+| `screwdriver_box` | fix required | <!-- TODO: what needs fixing --> |
+| `screwdriver_box_v1` | fix required | <!-- TODO: what needs fixing --> |
+| `stack_cubes` | Re-record | <!-- TODO: what needs fixing --> |
+| `cube` | fix required | <!-- TODO: what needs fixing --> |
+
+## Intended Use
+
+- Behaviour cloning and imitation learning on tabletop manipulation
+- Fine-tuning vision-language-action models on a consistent hardware setup
+- Multi-task and task-family transfer experiments
+- Benchmarking data-efficiency across task types
+- Teaching and coursework on robot learning pipelines
+
+## Data Collection
+
+All episodes were collected by **human teleoperation** on an **SO-101 (leader–follower pair)**
+setup. An operator moved the leader arm by hand while the follower arm mirrored the
+motion, and joint states, actions and synchronised video were recorded through LeRobot.
+No scripted or autonomous policies were used, so every trajectory reflects human
+timing, hesitation and correction.
+
+### Robot
+
+| Property | Value |
+|---|---|
+| Arm | SO-101 (leader–follower pair) |
+| Control | Leader arm (leader–follower joint mirroring) |
+| Recording framework | LeRobot |
+
+### Cameras
+
+Two cameras per episode, both the same sensor:
+
+| View | Sensor |
+|---|---|
+| Overhead | IMX335 5MP USB Camera (B), 5V USB 2.0, 175° wide angle |
+| Wrist | IMX335 5MP USB Camera (B), 5V USB 2.0, 175° wide angle |
+
+The **overhead** camera gives a fixed third-person view of the whole workspace; the
+**wrist** camera is mounted on the follower arm and moves with the end effector,
+providing close-range detail during grasps and contact. The 175° field of view keeps
+the full table in frame from a short mounting distance, at the cost of noticeable
+barrel distortion near the edges — the recordings are **not undistorted**, so
+calibrate or rectify yourself if your method assumes a pinhole model.
+
+Both streams were captured through LeRobot's OpenCV backend:
+
+```json
+{
+  "type": "opencv",
+  "index_or_path": "/dev/video2",
+  "width": 640,
+  "height": 480,
+  "fps": 30
+}
+```
+
+Recorded at **640 × 480 @ 30 fps** — well below the
+sensor's 5MP capability, chosen to keep two USB streams stable and file sizes
+manageable. Device indices vary between recording sessions; check
+`observation.images.*` in each folder's `meta/info.json` for the exact keys and shapes
+a given dataset uses.
+
+### Environment and randomisation
+
+In most datasets object positions were **randomised between runs**, so the policy
+cannot succeed by memorising a fixed layout. Randomisation was manual rather than
+programmatic, so coverage is uneven — some datasets vary position more aggressively
+than others.
+
+<!-- TODO: fill in the remaining environment details:
+     - Table surface and background
+     - Lighting conditions (fixed room lighting? natural light?)
+     - Object sets used per task family
+     - Recording period (dates) and number of operators
+     - Whether failed episodes were discarded or kept
+-->
+
+## Language Variants
+
+Not every folder is an independent recording. One dataset re-annotates episodes that
+already exist elsewhere in the collection:
+
+| Variant | Source episodes | Paraphrases | Difference |
+|---|---|---|---|
+| `pick_place_cubes_para` | `pick_place_cubes` | 8 | Same episodes, task string expressed 8 different ways |
+
+`pick_place_cubes_para` contains the **same trajectories** as `pick_place_cubes`, with
+the natural-language task description rewritten in 8 different phrasings. It exists to
+test whether a language-conditioned policy generalises across instruction wording
+rather than latching onto one exact string.
+
+Two consequences worth knowing:
+
+- **Do not count both toward dataset size.** Together they represent one set of
+  recordings, not two. The unique figures in the statistics table already exclude the
+  variant.
+- **Do not put both in the same training mix without thinking.** Naively concatenating
+  them duplicates every episode, doubling that task's weight. Either train on the
+  paraphrased version alone, or sample the pair as one dataset.
+
+## Limitations
+
+- **Single environment.** All recordings share one lab, one table and one lighting
+  setup. Background and surface diversity is effectively zero, so expect a large
+  sim-to-real-style gap when deploying elsewhere.
+- **Single embodiment.** Everything is SO-101. Cross-embodiment transfer is untested.
+- **Fixed camera geometry** at 640 × 480, with uncorrected wide-angle
+  distortion.
+- **Uneven dataset sizes.** Durations range from a few minutes to nearly an hour, so
+  naive concatenation heavily over-weights the longer datasets.
+- **Manual randomisation** means object-position coverage is not uniform or measured.
+- **Human demonstrations only** — no failure cases, recovery behaviours or
+  counterexamples, which limits use for methods that need negative data.
+
+<!-- TODO: add anything else you know to be true — e.g. whether any task
+     descriptions are inconsistent, or whether some datasets share episodes. -->
+
+## License
+
+Released under the **Apache 2.0** license. Individual datasets may carry additional
+attribution requirements.
+
+## Citation
+
+```bibtex
+@misc{niat10_2026,
+  title  = {NIAT10: A Tabletop Manipulation Dataset Collection},
+  author = {<!-- TODO: author list -->},
+  year   = {2026},
+  url    = {https://huggingface.co/datasets/NIATphysicalAI/NIAT10}
+}
+```
+
+## Related Resources
 
-Entries marked *fix required* or *Re-record* are included for completeness but have
-known issues; prefer the *Approved* subset for training runs.
+- [LeRobot](https://github.com/huggingface/lerobot) — framework used for recording
+- [LeRobot docs](https://huggingface.co/docs/lerobot) — full documentation
+- [Dataset format guide](https://huggingface.co/blog/lerobot-datasets) — best practices
+- [SmolVLA](https://huggingface.co/blog/smolvla) — VLA model these datasets suit
+- [Community Dataset v3](https://huggingface.co/datasets/HuggingFaceVLA/community_dataset_v3) — larger cross-embodiment collection