--- 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). # 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/} }