Datasets:

filesmuggler
/

experiment-cube-cup-100-l_diagonal-full

---
license: apache-2.0
task_categories:
- robotics
pretty_name: Cube-to-cup pick-and-place (L-shape + diagonal)
tags:
- LeRobot
- so100
- so101
- pick-and-place
- cube
- cup
- imitation-learning
- compositional-generalization
configs:
- config_name: default
  data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).

<a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path=filesmuggler/experiment-cube-cup-100-l_diagonal-full">
<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/>
<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/>
</a>

# Cube → cup pick-and-place (SO-ARM, L-shape + diagonal)

Teleoperated demonstrations of **picking a small dark cube and placing it into an orange cup** on a white table. Recorded on an SO-ARM follower (`robot_type: so_follower`) with LeRobot v3.

**Task:** `Grab the cube and put it in the cup`

![Typical start pose](assets/episode-000.png)
*Episode 0, top camera. Cube in the back row, cup in front-center (the most common cup cell).*

## Collection strategy

Start-pose sampling follows the **L** in-domain collection idea from Gao, Xie, Xiao, Finn, and Sadigh, [*Efficient Data Collection for Robotic Manipulation via Compositional Generalization*](https://iliad.stanford.edu/robot-data-comp/) (RSS 2024; [arXiv:2403.05110](https://arxiv.org/abs/2403.05110); [Hugging Face paper](https://huggingface.co/papers/2403.05110)).

That work treats environmental axes (object pose, container, texture, …) as a **factor grid**. Instead of recording every combination, an **L** covers each factor along one axis while holding the other at a base value, and relies on the policy to **compose** the missing pairs. This dataset applies that idea to two factors on a 3×3 table:

- **cube (target) cell**
- **cup (goal) cell**

Concretely:

1. **L, cube arm** — vary cube cell while the cup stays near a base place pose (here **goal cell 8**, 33 episodes).
2. **L, cup arm** — vary cup cell while the cube stays near a base pick pose (here **target cell 5**, 25 episodes).
3. **Diagonal / extra pairs** — additional cube→cup combinations off the L, so the dump is denser than a pure L (`l_diagonal-full` in the repo name).

A full 9×9 grid would be 81 pairs. This run has **35 / 81** observed pairs: the L plus a diagonal, not uniform coverage. That is intentional, not an accident.

## Why this dataset

- Single skill, repeated with **varied start poses** on a 3×3 table grid, collected to test composition rather than to fill every pair.
- Two RGB views: overhead `observation.images.top` and `observation.images.wrist` (640×480, AV1, 30 FPS).
- Joint-space `action` and `observation.state` (6-DoF + gripper, degrees).

Trained policies: [smolvla_cube_cup](https://huggingface.co/filesmuggler/smolvla_cube_cup), [act_cube_cup](https://huggingface.co/filesmuggler/act_cube_cup), [pi05_cube_cup](https://huggingface.co/filesmuggler/pi05_cube_cup).

## Snapshot

| | |
|---|---|
| Episodes | **99** (0–98). Filename says 100; one episode was not recorded. |
| Frames | 29,432 (~16.4 minutes at 30 FPS) |
| Episode length | min 6.5 s · median 9.7 s · max 19.5 s (episode 62) |
| Cameras | top, wrist |
| License | Apache-2.0 |

## Spatial coverage (first-frame review)

Every episode has a visible cube (target) and cup (goal) on frame 0. Counts below are **not** uniform, and they match the L: mass on **T5** and **G8**.

**Cube (target) cell counts** (1 = back-left, 9 = front-right):

| | left | mid | right |
|---|---:|---:|---:|
| back | 3 | 7 | 8 |
| mid | 13 | **25** | 14 |
| front | 6 | 12 | 11 |

**Cup (goal) cell counts:**

| | left | mid | right |
|---|---:|---:|---:|
| back | 12 | 8 | 8 |
| mid | 8 | 7 | 5 |
| front | 8 | **33** | 10 |

- Unique cube→cup pairs: **35 / 81**
- Most common pair: cube cell 8 → cup cell 2 (7 episodes)
- 32 cube locations were boxed by hand (dark cube vs white table); cups were automatic
- Same-cell starts (cube and cup share a cell): episodes 46, 47, 80, 81, 82, 87

![Sample distribution](assets/l_diagonal_full_coverage.png)
*Bar chart of target (green) and goal (blue) first-frame counts. Orange dots mark the annotated robot.*

## Intended use and limits

**Use for:** imitation learning / VLA fine-tuning on a short-horizon pick-and-place skill, and for studying whether a policy can compose unseen cube/cup cell pairs from L + diagonal data (the setting Gao et al. describe).

**Limits:**

- One table, one lighting setup, one cube, one cup.
- Pair coverage has large holes (especially picks from cell 1 and places away from cell 8). That is by design for an L, not a claim of full combinatorial coverage.
- This is an independent SO-ARM dataset inspired by their collection strategy; it is not the WidowX / kitchen data from the paper, and we do not use BridgeData V2 here.
- First-frame overlays are a review aid, not ground-truth object poses. A few automatic cube locks are ambiguous when the cube is near the gripper.
- Joint values are in the LeRobot degree convention; absolute table XYZ from nominal FK is approximate.

## Dataset Structure

See [meta/info.json](meta/info.json) for the full LeRobot v3 schema (`codebase_version: v3.0`).

## Citation

```bibtex
@misc{filesmuggler_cube_cup_l_diagonal_full,
  title  = {Cube-to-cup pick-and-place (L-shape + diagonal)},
  author = {filesmuggler},
  year   = {2026},
  url    = {https://huggingface.co/datasets/filesmuggler/experiment-cube-cup-100-l_diagonal-full},
  note   = {Collection layout follows the L strategy of Gao et al., RSS 2024}
}
@inproceedings{Gao-RSS-24,
  author    = {Jensen Gao and Annie Xie and Ted Xiao and Chelsea Finn and Dorsa Sadigh},
  title     = {Efficient Data Collection for Robotic Manipulation via Compositional Generalization},
  booktitle = {Proceedings of Robotics: Science and Systems},
  year      = {2024},
  address   = {Delft, Netherlands},
  month     = {July},
  doi       = {10.15607/RSS.2024.XX.013},
  url       = {https://iliad.stanford.edu/robot-data-comp/}
}

	---
	license: apache-2.0
	task_categories:
	- robotics
	pretty_name: Cube-to-cup pick-and-place (L-shape + diagonal)
	tags:
	- LeRobot
	- so100
	- so101
	- pick-and-place
	- cube
	- cup
	- imitation-learning
	- compositional-generalization
	configs:
	- config_name: default
	data_files: data//.parquet
	---

	This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).

	<a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path=filesmuggler/experiment-cube-cup-100-l_diagonal-full">
	<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/>
	<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/>
	</a>

	# Cube → cup pick-and-place (SO-ARM, L-shape + diagonal)

	Teleoperated demonstrations of picking a small dark cube and placing it into an orange cup on a white table. Recorded on an SO-ARM follower (`robot_type: so_follower`) with LeRobot v3.

	Task: `Grab the cube and put it in the cup`

	![Typical start pose](assets/episode-000.png)
	Episode 0, top camera. Cube in the back row, cup in front-center (the most common cup cell).

	## Collection strategy

	Start-pose sampling follows the L in-domain collection idea from Gao, Xie, Xiao, Finn, and Sadigh, [Efficient Data Collection for Robotic Manipulation via Compositional Generalization](https://iliad.stanford.edu/robot-data-comp/) (RSS 2024; [arXiv:2403.05110](https://arxiv.org/abs/2403.05110); [Hugging Face paper](https://huggingface.co/papers/2403.05110)).

	That work treats environmental axes (object pose, container, texture, …) as a factor grid. Instead of recording every combination, an L covers each factor along one axis while holding the other at a base value, and relies on the policy to compose the missing pairs. This dataset applies that idea to two factors on a 3×3 table:

	- cube (target) cell
	- cup (goal) cell

	Concretely:

	1. L, cube arm — vary cube cell while the cup stays near a base place pose (here goal cell 8, 33 episodes).
	2. L, cup arm — vary cup cell while the cube stays near a base pick pose (here target cell 5, 25 episodes).
	3. Diagonal / extra pairs — additional cube→cup combinations off the L, so the dump is denser than a pure L (`l_diagonal-full` in the repo name).

	A full 9×9 grid would be 81 pairs. This run has 35 / 81 observed pairs: the L plus a diagonal, not uniform coverage. That is intentional, not an accident.

	## Why this dataset

	- Single skill, repeated with varied start poses on a 3×3 table grid, collected to test composition rather than to fill every pair.
	- Two RGB views: overhead `observation.images.top` and `observation.images.wrist` (640×480, AV1, 30 FPS).
	- Joint-space `action` and `observation.state` (6-DoF + gripper, degrees).

	Trained policies: [smolvla_cube_cup](https://huggingface.co/filesmuggler/smolvla_cube_cup), [act_cube_cup](https://huggingface.co/filesmuggler/act_cube_cup), [pi05_cube_cup](https://huggingface.co/filesmuggler/pi05_cube_cup).

	## Snapshot

	\| \| \|
	\|---\|---\|
	\| Episodes \| 99 (0–98). Filename says 100; one episode was not recorded. \|
	\| Frames \| 29,432 (~16.4 minutes at 30 FPS) \|
	\| Episode length \| min 6.5 s · median 9.7 s · max 19.5 s (episode 62) \|
	\| Cameras \| top, wrist \|
	\| License \| Apache-2.0 \|

	## Spatial coverage (first-frame review)

	Every episode has a visible cube (target) and cup (goal) on frame 0. Counts below are not uniform, and they match the L: mass on T5 and G8.

	Cube (target) cell counts (1 = back-left, 9 = front-right):

	\| \| left \| mid \| right \|
	\|---\|---:\|---:\|---:\|
	\| back \| 3 \| 7 \| 8 \|
	\| mid \| 13 \| 25 \| 14 \|
	\| front \| 6 \| 12 \| 11 \|

	Cup (goal) cell counts:

	\| \| left \| mid \| right \|
	\|---\|---:\|---:\|---:\|
	\| back \| 12 \| 8 \| 8 \|
	\| mid \| 8 \| 7 \| 5 \|
	\| front \| 8 \| 33 \| 10 \|

	- Unique cube→cup pairs: 35 / 81
	- Most common pair: cube cell 8 → cup cell 2 (7 episodes)
	- 32 cube locations were boxed by hand (dark cube vs white table); cups were automatic
	- Same-cell starts (cube and cup share a cell): episodes 46, 47, 80, 81, 82, 87

	![Sample distribution](assets/l_diagonal_full_coverage.png)
	Bar chart of target (green) and goal (blue) first-frame counts. Orange dots mark the annotated robot.

	## Intended use and limits

	Use for: imitation learning / VLA fine-tuning on a short-horizon pick-and-place skill, and for studying whether a policy can compose unseen cube/cup cell pairs from L + diagonal data (the setting Gao et al. describe).

	Limits:

	- One table, one lighting setup, one cube, one cup.
	- Pair coverage has large holes (especially picks from cell 1 and places away from cell 8). That is by design for an L, not a claim of full combinatorial coverage.
	- This is an independent SO-ARM dataset inspired by their collection strategy; it is not the WidowX / kitchen data from the paper, and we do not use BridgeData V2 here.
	- First-frame overlays are a review aid, not ground-truth object poses. A few automatic cube locks are ambiguous when the cube is near the gripper.
	- Joint values are in the LeRobot degree convention; absolute table XYZ from nominal FK is approximate.

	## Dataset Structure

	See [meta/info.json](meta/info.json) for the full LeRobot v3 schema (`codebase_version: v3.0`).

	## Citation

	```bibtex
	@misc{filesmuggler_cube_cup_l_diagonal_full,
	title = {Cube-to-cup pick-and-place (L-shape + diagonal)},
	author = {filesmuggler},
	year = {2026},
	url = {https://huggingface.co/datasets/filesmuggler/experiment-cube-cup-100-l_diagonal-full},
	note = {Collection layout follows the L strategy of Gao et al., RSS 2024}
	}

	@inproceedings{Gao-RSS-24,
	author = {Jensen Gao and Annie Xie and Ted Xiao and Chelsea Finn and Dorsa Sadigh},
	title = {Efficient Data Collection for Robotic Manipulation via Compositional Generalization},
	booktitle = {Proceedings of Robotics: Science and Systems},
	year = {2024},
	address = {Delft, Netherlands},
	month = {July},
	doi = {10.15607/RSS.2024.XX.013},
	url = {https://iliad.stanford.edu/robot-data-comp/}
	}