NIAT10 — A Tabletop Manipulation Dataset Collection

25 teleoperated robot manipulation datasets recorded at NIAT, consolidated into a single repository for imitation-learning and vision-language-action research.

Overview

NIAT10 gathers every manipulation dataset recorded by the NIAT Physical AI group into one place, each one preserved in its original 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.

Dataset Statistics

Metric	Value
Total datasets	25
Total episodes	2,138
Total frames	1,021,300
Recorded duration	9.46 hours (09:27:23)
Video footage	18.91 hours across all camera streams
Camera frames	2,042,600
Task families	5
Robot embodiments	1
Average duration/dataset	0.38 hours
Datasets passing review	23 of 25

Recorded duration is wall-clock robot interaction time, computed as total_frames / fps from each dataset's meta/info.json, so it reflects the current contents of each repo after episode pruning. Video footage counts each synchronised camera stream separately — it is the volume of video you will download and decode, not additional robot time.

pick_place_cubes_para re-annotates episodes that already appear elsewhere in the collection rather than adding new recordings (see Language variants). Excluding it, the collection holds 8.60 hours of distinct recordings across 1,838 episodes.

Task Distribution

Task family	Datasets	Episodes	Duration	% of data
Pick and place	14	1210	05:01:17	53.1%
Sorting	4	374	02:08:26	22.6%
Stacking	3	212	00:56:42	10.0%
Non-prehensile push	2	244	00:49:07	8.7%
Orientation	2	98	00:31:52	5.6%

Robot Types

Robot type	Datasets	%
so_follower	25	100.0%

Recorded with LeRobot v3.0 (25 datasets). Frame rates present: 30 fps (25).

Contributors

Recorded and published under the accounts below.

Account	Datasets	%
NIATphysicalAI	20	80.0%
Bradx86	5	20.0%

Please credit the individual contributors above, not only the publishing accounts, when using this collection.

Contents

#	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
2	Battery_sort	Sorting	106	93,878	00:52:09	30	OK	Battery_sort
3	Battery_sort_v1	Sorting	7	5,681	00:03:09	30	OK	Battery_sort_v1
4	drawer_cube_screwdriver	Pick and place	101	54,895	00:30:30	30	OK	drawer_cube_screwdriver
5	Drawer_screwdriver_v2	Pick and place	51	30,709	00:17:04	30	OK	Drawer_screwdriver_v2
6	Drawer_screwdriver	Pick and place	34	23,221	00:12:54	30	OK	Drawer_screwdriver
7	screwdriver_box	Pick and place	47	27,536	00:15:18	30	OK	screwdriver_box
8	screwdriver_box_v1	Pick and place	20	13,829	00:07:41	30	OK	screwdriver_box_v1
9	screwdriver_box_v0	Pick and place	10	7,498	00:04:10	30	OK	screwdriver_box_v0
10	stack_cubes	Stacking	58	30,806	00:17:07	30	OK	stack_cubes
11	Cup_stack	Stacking	100	45,646	00:25:22	30	OK	Cup_stack
12	eraser	Pick and place	60	26,893	00:14:56	30	OK	eraser
13	cube	Stacking	54	25,595	00:14:13	30	fix required	cube
14	stapler	Pick and place	37	16,750	00:09:18	30	OK	stapler
15	bottle	Orientation	48	25,162	00:13:59	30	OK	bottle
16	penholder	Sorting	92	43,990	00:24:26	30	OK	penholder
17	sort_biodegradable	Sorting	169	87,635	00:48:41	30	OK	sort_biodegradable
18	pick_place_cubes_para	Pick and place	300	92,287	00:51:16	30	OK	pick_place_cubes_para
19	pick_place_cubes	Pick and place	300	92,287	00:51:16	30	OK	pick_place_cubes
20	chocolate_pick_place	Pick and place	50	29,178	00:16:13	30	OK	chocolate_pick_place
21	pick_place	Pick and place	50	18,559	00:10:19	30	OK	pick-place
22	push_t_v2	Non-prehensile push	194	79,027	00:43:54	30	OK	push_t_v2
23	t_push	Non-prehensile push	50	9,388	00:05:13	30	OK	t_push
24	Bottle_orient	Orientation	50	32,196	00:17:53	30	OK	bottle-test
25	Cube_pick_place	Pick and place	50	28,764	00:15:59	30	OK	record-test

Repository Structure

NIAT10/
├── <dataset_name>/
│   ├── 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
└── ...

Usage

Authenticate

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:

hf download NIATphysicalAI/NIAT10 \
    --repo-type=dataset \
    --include "Battery_sort/*" \
    --local-dir ./NIAT10

Download everything

hf download NIATphysicalAI/NIAT10 --repo-type=dataset --local-dir ./NIAT10

Load with LeRobot

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

from pathlib import Path
import json

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")

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:

from datasets import load_dataset

ds = load_dataset("NIATphysicalAI/NIAT10", "Battery_sort", split="train")

Training

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. 2 of 25 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 (25) — 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	Dataset card needs correcting
cube	fix required	Frozen camera2 feed on some episodes

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.

Cropped overhead views. Some datasets have a cropped overhead feed, done to remove distractors and give a bounded region of interest for training. Original 1920×1080 frames were cropped to a 955×720 region of interest (margins: 580 left, 385 right, 180 top and bottom) and downsampled to 640×480. The wrist view is uncropped. Because the crop changes the effective field of view and scale, a policy trained on cropped datasets will not transfer cleanly to uncropped ones without matching the preprocessing.

Both streams were captured through LeRobot's OpenCV backend:

{
  "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.

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

• Environment. Most recordings share one lab, one table and similar lighting. Background and surface diversity is limited.
• Single embodiment. Everything is SO-101. Cross-embodiment transfer is untested.
• Fixed camera geometry at 640 × 480, with uncorrected wide-angle distortion, and inconsistent overhead cropping between datasets.
• 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; in some datasets objects were re-randomised only every ~10 episodes.
• Human demonstrations only — no failure cases, recovery behaviours or counterexamples, which limits use for methods that need negative data.

License

Released under the Apache 2.0 license. Individual datasets may carry additional attribution requirements.

Related Resources

• LeRobot — framework used for recording
• LeRobot docs — full documentation
• Dataset format guide — best practices
• SmolVLA — VLA model these datasets suit
• Community Dataset v3 — larger cross-embodiment collection