Datasets:

DecisionFacts
/

Physical_AI_SO101_Test_Tube_Nesting_Task

---
license: cc-by-nc-4.0
task_categories:
  - robotics
language:
  - en
size_categories:
  - 100K<n<1M
tags:
  - robotics
  - teleoperation
  - imitation-learning
  - manipulation
  - lerobot
  - so-101
  - vla
  - world-model
  - laboratory-automation
  - test-tube
  - nesting
  - GR00T
  - Isaac-compatible
  - tabular
  - timeseries
  - video
  - parquet
  - dask
pretty_name: DecisionFacts Physical AI — SO-101 Test Tube Nesting
configs:
  - config_name: default
    data_files: data/*/*.parquet
---

# DecisionFacts Physical AI — SO-101 Test Tube Nesting

Real-world, human-teleoperated demonstrations of an **SO-101 arm nesting test tubes**, recorded in [LeRobotDataset v3.0](https://huggingface.co/docs/lerobot/main/en/lerobot-dataset-v3) format with two synchronized RGB cameras, joint state and action at 30 fps. This repository is a public evaluation sample of the DecisionFacts Physical AI catalog.

| | |
|---|---|
| **Robot** | SO-101 follower (`so_follower`), 5-DoF arm + gripper, single embodiment |
| **Episodes** | 121 |
| **Frames** | 179,127 (≈ 100 minutes at 30 fps) |
| **Episode length** | ≈ 54 s on average, 60 s maximum |
| **Tasks** | 2 language-annotated task variants (see below) |
| **Cameras** | `cam_front`, `cam_top` — 640×480 RGB, 30 fps, AV1 |
| **Format** | LeRobotDataset v3.0 (Parquet + MP4) |
| **Split** | `train` only (episodes 0–120) |
| **License** | CC BY-NC 4.0 (research and evaluation) |

## Task

The operator picks up test tubes and nests them into `<TARGET — e.g. a rack / a larger tube>` on a tabletop workspace. The task stresses skills that matter for lab-automation and fine-manipulation policies:

- **Precise grasping of thin, cylindrical objects** with a small gripper footprint.
- **Tight-tolerance insertion**: the tube must be aligned with the opening before release, so small orientation errors cause failure.
- **Multi-step sequencing** within a single, roughly minute-long episode, which makes the data useful for long-horizon policy and world-model evaluation.

Language instructions (from `meta/tasks.parquet`):

| `task_index` | Instruction | Share of frames |
|---:|---|---:|
| 0 | `<TASK 0 INSTRUCTION — copy verbatim from meta/tasks.parquet>` | ≈ 40% |
| 1 | `<TASK 1 INSTRUCTION — copy verbatim from meta/tasks.parquet>` | ≈ 60% |

## Collection protocol

- **Expert teleoperation.** Each episode is a goal-directed demonstration by a trained operator using a leader arm; no scripted or simulated motion.
- **Synchronized capture.** Joint state, commanded action and both camera streams are recorded in lock-step at 30 fps.
- **Review before release.** Episodes are checked for completeness, timing alignment and coherent execution of the intended task before release.

## Data structure

```
meta/
  info.json            # schema, fps, codec, paths
  stats.json           # per-feature normalization statistics
  tasks.parquet        # task_index -> language instruction
  episodes/chunk-000/  # per-episode metadata (length, file offsets, stats)
data/chunk-000/        # frame-level Parquet (state, action, indices)
videos/
  observation.images.cam_front/chunk-000/*.mp4
  observation.images.cam_top/chunk-000/*.mp4
```

### Features

| Feature | Type | Shape | Description |
|---|---|---|---|
| `observation.state` | float32 | 6 | Measured follower joint positions |
| `action` | float32 | 6 | Commanded joint positions from the leader arm |
| `observation.images.cam_front` | video | 480×640×3 | Front view of the workspace |
| `observation.images.cam_top` | video | 480×640×3 | Overhead view of the workspace |
| `timestamp` | float32 | 1 | Seconds since the start of the episode |
| `frame_index` | int64 | 1 | Frame number within the episode |
| `episode_index` | int64 | 1 | Episode ID (0–103) |
| `index` | int64 | 1 | Global frame ID |
| `task_index` | int64 | 1 | Key into `meta/tasks.parquet` |

Joint order for `observation.state` and `action`:

`shoulder_pan.pos`, `shoulder_lift.pos`, `elbow_flex.pos`, `wrist_flex.pos`, `wrist_roll.pos`, `gripper.pos`

Arm joints are in degrees; the gripper is on a 0–100 scale (0 = closed). Observed ranges (from `meta/stats.json`):

| Joint | State min | State max | State mean |
|---|---:|---:|---:|
| shoulder_pan | −87.6 | 47.6 | −2.9 |
| shoulder_lift | −105.1 | 74.5 | −7.5 |
| elbow_flex | −81.1 | 99.3 | 30.3 |
| wrist_flex | −96.1 | 99.5 | 7.8 |
| wrist_roll | −131.5 | 165.9 | 77.9 |
| gripper | 1.7 | 57.1 | 15.8 |
The commanded gripper value is typically lower than the measured one while an object is held (median 7.9 vs 13.1), which is expected: the leader commands a tighter grip than the follower can reach around a tube.
### Video encoding
Both streams are AV1 (`yuv420p`, CRF 30, GOP 2), decoded with PyAV or TorchCodec. The short GOP keeps random frame access fast during training. If your pipeline cannot decode AV1, re-encode to H.264 with `ffmpeg -c:v libx264`.
## Usage
### LeRobot
```python
from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("DecisionFacts/Physical_AI_SO101_Test_Tube_Nesting_Task")
print(ds.num_episodes, ds.num_frames, ds.fps)

frame = ds[0]
frame["observation.state"]              # tensor(6)
frame["action"]                         # tensor(6)
frame["observation.images.cam_front"]   # tensor(3, 480, 640)
frame["task"]                           # language instruction
```
Train a policy (example: ACT):
```bash
lerobot-train \
  --dataset.repo_id=DecisionFacts/Physical_AI_SO101_Test_Tube_Nesting_Task \
  --policy.type=act \
  --output_dir=outputs/test_tube_act
```
### Tabular data only (no video)
```python
from datasets import load_dataset

ds = load_dataset("DecisionFacts/Physical_AI_SO101_Test_Tube_Nesting_Task", split="train")
```
### Visualize
Browse episodes in the [LeRobot dataset visualizer](https://huggingface.co/spaces/lerobot/visualize_dataset?path=%2FDecisionFacts%2FPhysical_AI_SO101_Test_Tube_Nesting_Task).
## Intended uses
- Imitation-learning baselines (ACT, Diffusion Policy) for precise pick-and-insert.
- Fine-tuning Vision-Language-Action models (e.g. SmolVLA, π0) on a laboratory-style manipulation task.
- Evaluating world models and video-prediction models on long-horizon, contact-rich manipulation.
## Limitations
- **Single embodiment and setup.** All data comes from one SO-101 arm in one workspace with fixed camera placement; expect a drop in performance under new lighting, backgrounds or camera poses.
- **Two camera views, RGB only.** No depth or wrist camera; transparent tubes can be hard to localize.
- **Joint-space only.** No end-effector poses, forces or tactile signals.
- **Evaluation sample.** 104 episodes are enough for fine-tuning and benchmarking, not for training a general policy from scratch.
- **No predefined validation split.** Hold out episodes yourself (for example the last 10%).
## License and access
This repository is released under **CC BY-NC 4.0** for research and evaluation. Commercial use, redistribution, or training models intended for commercial deployment requires a separate license (see below).
## Licensing the full catalog
This repository is a sample of a larger, continuously growing catalog. Related public samples on SO-101:
- [Cup Nesting](https://huggingface.co/datasets/DecisionFacts/Physical_AI_SO101_Cup_Nesting_Task)
- [Towel Folding](https://huggingface.co/datasets/DecisionFacts/Physical_AI_SO101_Towel_Folding_Task)
- [Stationary Sorting](https://huggingface.co/datasets/DecisionFacts/Physical_AI_SO101_Stationary_Sorting_Task)
- [Pipe and Joint Sorting](https://huggingface.co/datasets/DecisionFacts/Physical_AI_SO101_Pipe_And_Joint_Sorting_Task)
We offer:
- **Pilot tranches** (200–500 episodes) for evaluation and fine-tuning experiments
- **Production tranches** (1,000+ episodes) for training and post-training at scale
- **Custom collection** for new tasks, environments, diversity requirements or other embodiments
Contact **info@decisionfacts.io** to scope access.
## Citation
```bibtex
@misc{decisionfacts_so101_test_tube_nesting_2026,
  title        = {DecisionFacts Physical AI: SO-101 Test Tube Nesting},
  author       = {Raghav, Prabhu and Unni, Sreeram B and Gopalan, Sriram and Kumar, Saravana},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/DecisionFacts/Physical_AI_SO101_Test_Tube_Nesting_Task}}
}
```

	---
	license: cc-by-nc-4.0
	task_categories:
	- robotics
	language:
	- en
	size_categories:
	- 100K<n<1M
	tags:
	- robotics
	- teleoperation
	- imitation-learning
	- manipulation
	- lerobot
	- so-101
	- vla
	- world-model
	- laboratory-automation
	- test-tube
	- nesting
	- GR00T
	- Isaac-compatible
	- tabular
	- timeseries
	- video
	- parquet
	- dask
	pretty_name: DecisionFacts Physical AI — SO-101 Test Tube Nesting
	configs:
	- config_name: default
	data_files: data//.parquet
	---


	# DecisionFacts Physical AI — SO-101 Test Tube Nesting

	Real-world, human-teleoperated demonstrations of an SO-101 arm nesting test tubes, recorded in [LeRobotDataset v3.0](https://huggingface.co/docs/lerobot/main/en/lerobot-dataset-v3) format with two synchronized RGB cameras, joint state and action at 30 fps. This repository is a public evaluation sample of the DecisionFacts Physical AI catalog.

	\| \| \|
	\|---\|---\|
	\| Robot \| SO-101 follower (`so_follower`), 5-DoF arm + gripper, single embodiment \|
	\| Episodes \| 121 \|
	\| Frames \| 179,127 (≈ 100 minutes at 30 fps) \|
	\| Episode length \| ≈ 54 s on average, 60 s maximum \|
	\| Tasks \| 2 language-annotated task variants (see below) \|
	\| Cameras \| `cam_front`, `cam_top` — 640×480 RGB, 30 fps, AV1 \|
	\| Format \| LeRobotDataset v3.0 (Parquet + MP4) \|
	\| Split \| `train` only (episodes 0–120) \|
	\| License \| CC BY-NC 4.0 (research and evaluation) \|

	## Task

	The operator picks up test tubes and nests them into `<TARGET — e.g. a rack / a larger tube>` on a tabletop workspace. The task stresses skills that matter for lab-automation and fine-manipulation policies:

	- Precise grasping of thin, cylindrical objects with a small gripper footprint.
	- Tight-tolerance insertion: the tube must be aligned with the opening before release, so small orientation errors cause failure.
	- Multi-step sequencing within a single, roughly minute-long episode, which makes the data useful for long-horizon policy and world-model evaluation.

	Language instructions (from `meta/tasks.parquet`):

	\| `task_index` \| Instruction \| Share of frames \|
	\|---:\|---\|---:\|
	\| 0 \| `<TASK 0 INSTRUCTION — copy verbatim from meta/tasks.parquet>` \| ≈ 40% \|
	\| 1 \| `<TASK 1 INSTRUCTION — copy verbatim from meta/tasks.parquet>` \| ≈ 60% \|

	## Collection protocol

	- Expert teleoperation. Each episode is a goal-directed demonstration by a trained operator using a leader arm; no scripted or simulated motion.
	- Synchronized capture. Joint state, commanded action and both camera streams are recorded in lock-step at 30 fps.
	- Review before release. Episodes are checked for completeness, timing alignment and coherent execution of the intended task before release.

	## Data structure

	```
	meta/
	info.json # schema, fps, codec, paths
	stats.json # per-feature normalization statistics
	tasks.parquet # task_index -> language instruction
	episodes/chunk-000/ # per-episode metadata (length, file offsets, stats)
	data/chunk-000/ # frame-level Parquet (state, action, indices)
	videos/
	observation.images.cam_front/chunk-000/*.mp4
	observation.images.cam_top/chunk-000/*.mp4
	```

	### Features

	\| Feature \| Type \| Shape \| Description \|
	\|---\|---\|---\|---\|
	\| `observation.state` \| float32 \| 6 \| Measured follower joint positions \|
	\| `action` \| float32 \| 6 \| Commanded joint positions from the leader arm \|
	\| `observation.images.cam_front` \| video \| 480×640×3 \| Front view of the workspace \|
	\| `observation.images.cam_top` \| video \| 480×640×3 \| Overhead view of the workspace \|
	\| `timestamp` \| float32 \| 1 \| Seconds since the start of the episode \|
	\| `frame_index` \| int64 \| 1 \| Frame number within the episode \|
	\| `episode_index` \| int64 \| 1 \| Episode ID (0–103) \|
	\| `index` \| int64 \| 1 \| Global frame ID \|
	\| `task_index` \| int64 \| 1 \| Key into `meta/tasks.parquet` \|

	Joint order for `observation.state` and `action`:

	`shoulder_pan.pos`, `shoulder_lift.pos`, `elbow_flex.pos`, `wrist_flex.pos`, `wrist_roll.pos`, `gripper.pos`

	Arm joints are in degrees; the gripper is on a 0–100 scale (0 = closed). Observed ranges (from `meta/stats.json`):

	\| Joint \| State min \| State max \| State mean \|
	\|---\|---:\|---:\|---:\|
	\| shoulder_pan \| −87.6 \| 47.6 \| −2.9 \|
	\| shoulder_lift \| −105.1 \| 74.5 \| −7.5 \|
	\| elbow_flex \| −81.1 \| 99.3 \| 30.3 \|
	\| wrist_flex \| −96.1 \| 99.5 \| 7.8 \|
	\| wrist_roll \| −131.5 \| 165.9 \| 77.9 \|
	\| gripper \| 1.7 \| 57.1 \| 15.8 \|

	The commanded gripper value is typically lower than the measured one while an object is held (median 7.9 vs 13.1), which is expected: the leader commands a tighter grip than the follower can reach around a tube.

	### Video encoding

	Both streams are AV1 (`yuv420p`, CRF 30, GOP 2), decoded with PyAV or TorchCodec. The short GOP keeps random frame access fast during training. If your pipeline cannot decode AV1, re-encode to H.264 with `ffmpeg -c:v libx264`.

	## Usage

	### LeRobot

	```python
	from lerobot.datasets.lerobot_dataset import LeRobotDataset

	ds = LeRobotDataset("DecisionFacts/Physical_AI_SO101_Test_Tube_Nesting_Task")
	print(ds.num_episodes, ds.num_frames, ds.fps)

	frame = ds[0]
	frame["observation.state"] # tensor(6)
	frame["action"] # tensor(6)
	frame["observation.images.cam_front"] # tensor(3, 480, 640)
	frame["task"] # language instruction
	```

	Train a policy (example: ACT):

	```bash
	lerobot-train \
	--dataset.repo_id=DecisionFacts/Physical_AI_SO101_Test_Tube_Nesting_Task \
	--policy.type=act \
	--output_dir=outputs/test_tube_act
	```

	### Tabular data only (no video)

	```python
	from datasets import load_dataset

	ds = load_dataset("DecisionFacts/Physical_AI_SO101_Test_Tube_Nesting_Task", split="train")
	```

	### Visualize

	Browse episodes in the [LeRobot dataset visualizer](https://huggingface.co/spaces/lerobot/visualize_dataset?path=%2FDecisionFacts%2FPhysical_AI_SO101_Test_Tube_Nesting_Task).

	## Intended uses

	- Imitation-learning baselines (ACT, Diffusion Policy) for precise pick-and-insert.
	- Fine-tuning Vision-Language-Action models (e.g. SmolVLA, π0) on a laboratory-style manipulation task.
	- Evaluating world models and video-prediction models on long-horizon, contact-rich manipulation.

	## Limitations

	- Single embodiment and setup. All data comes from one SO-101 arm in one workspace with fixed camera placement; expect a drop in performance under new lighting, backgrounds or camera poses.
	- Two camera views, RGB only. No depth or wrist camera; transparent tubes can be hard to localize.
	- Joint-space only. No end-effector poses, forces or tactile signals.
	- Evaluation sample. 104 episodes are enough for fine-tuning and benchmarking, not for training a general policy from scratch.
	- No predefined validation split. Hold out episodes yourself (for example the last 10%).

	## License and access

	This repository is released under CC BY-NC 4.0 for research and evaluation. Commercial use, redistribution, or training models intended for commercial deployment requires a separate license (see below).

	## Licensing the full catalog

	This repository is a sample of a larger, continuously growing catalog. Related public samples on SO-101:

	- [Cup Nesting](https://huggingface.co/datasets/DecisionFacts/Physical_AI_SO101_Cup_Nesting_Task)
	- [Towel Folding](https://huggingface.co/datasets/DecisionFacts/Physical_AI_SO101_Towel_Folding_Task)
	- [Stationary Sorting](https://huggingface.co/datasets/DecisionFacts/Physical_AI_SO101_Stationary_Sorting_Task)
	- [Pipe and Joint Sorting](https://huggingface.co/datasets/DecisionFacts/Physical_AI_SO101_Pipe_And_Joint_Sorting_Task)

	We offer:

	- Pilot tranches (200–500 episodes) for evaluation and fine-tuning experiments
	- Production tranches (1,000+ episodes) for training and post-training at scale
	- Custom collection for new tasks, environments, diversity requirements or other embodiments

	Contact info@decisionfacts.io to scope access.

	## Citation

	```bibtex
	@misc{decisionfacts_so101_test_tube_nesting_2026,
	title = {DecisionFacts Physical AI: SO-101 Test Tube Nesting},
	author = {Raghav, Prabhu and Unni, Sreeram B and Gopalan, Sriram and Kumar, Saravana},
	year = {2026},
	publisher = {Hugging Face},
	howpublished = {\url{https://huggingface.co/datasets/DecisionFacts/Physical_AI_SO101_Test_Tube_Nesting_Task}}
	}
	```