| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - robotics |
| tags: |
| - robotics |
| - manipulation |
| - lerobot |
| - droid |
| - physical-intelligence |
| - imitation-learning |
| - franka |
| size_categories: |
| - 10K<n<100K |
| pretty_name: MatrixNorm RoboticsGraph · DROID (free 327-episode sample) |
| --- |
| |
| # RoboticsGraph · DROID — FREE SAMPLE (327 episodes, videos included) |
|
|
| **This is a free sample of a much larger commercial release.** The full version — |
| **1,700,000 frames / 5,819 episodes / 70 collection sites**, with a |
| 13.7M-edge relationship graph and full video linkage — is available for |
| purchase. Contact: **energea@energeatech.com**. |
|
|
| **MatrixNorm RoboticsGraph** turns raw robot-learning corpora into |
| training-ready, rights-tracked, leakage-safe datasets: the verified chain |
| `instruction → observation → state → action → outcome` as structured rows — |
| not raw robot video dumps. |
|
|
| ## What's in the sample |
|
|
| 327 real-world Franka manipulation episodes (96,183 timesteps, 45 collection |
| sites) — and **every sample episode's exterior-camera video is included and |
| frame-addressable** from the data rows. |
|
|
| | file | what it is | |
| |---|---| |
| | `roboticsgraph_dataset.jsonl` / `.csv` | THE flat normalized table — one row per timestep, 38 columns | |
| | `training_tasks.jsonl.gz` | 96,183 training tasks: instruction + full robot_state → action vector | |
| | `train.jsonl.gz` / `validation.jsonl.gz` / `test.jsonl.gz` | the same tasks, materialized by episode-grouped split (220/47/60 episodes — zero episode leakage) | |
| | `eval_tasks.jsonl` | 327 held-out `predict_success` eval tasks | |
| | `hin_edges.jsonl.gz` | 774,067-edge relationship graph (episode/timestep/state/action/outcome/task) | |
| | `videos/` + `videos_index.*` | the covering exterior-camera MP4 + per-episode/per-camera windows; `video_frame_timestamp` on every row seeks straight to the frame | |
| | `source_manifest.json` / `rights_manifest.csv` | provenance, license trail, raw-archive SHA256 | |
|
|
| ```python |
| from datasets import load_dataset |
| rows = load_dataset("json", data_files="roboticsgraph_dataset.jsonl", split="train") |
| tasks = load_dataset("json", data_files="train.jsonl.gz", split="train") |
| ``` |
|
|
| ## Field coverage (this sample) |
|
|
| - `robot_embodiment` (Franka, verified from source metadata): **100%** |
| - state / end-effector pose / gripper / action vectors: **100%** |
| - video linkage (file + exact frame timestamp per row): **100%** |
| - collection site + collector provenance: **100%** |
| - outcome (success/failure ground truth): **100%** |
| - language instruction: **72.8%** — the remainder have no label anywhere in the |
| source; they are flagged, never invented, and their tasks are typed |
| `observation_to_action`. Alternate source phrasings are preserved. |
|
|
| ## Why this beats using the raw source directly |
|
|
| 1. **Training-ready tasks** — every timestep is already an |
| (instruction, robot_state, video-frame ref) → action_vector pair; no |
| parsing, joining, or episode reconstruction needed. |
| 2. **Leakage-safe splits** — episode-grouped, hash-materialized, auditable via |
| `split_group_key`. No silent train/test contamination. |
| 3. **Honesty contract** — every label traces to a real source field; gaps are |
| flagged, not filled. Two corrupt source columns were detected and excluded |
| (documented below). |
| 4. **Rights tracked end-to-end** — apache-2.0 verified at the source, rights |
| manifest + raw-archive SHA256 included. |
| 5. **Relationship graph** — HIN edges connect timesteps, states, actions, |
| outcomes and tasks for graph-based learning and retrieval. |
|
|
| ## The full release (for sale) |
|
|
| - **1,700,000 frames / 5,819 episodes / 70 sites** (≈18× this sample) |
| - 1.7M training tasks + 5,819 eval tasks, 13,682,009 HIN edges |
| - full video index for all episodes × 3 cameras (exterior 1/2 + wrist) |
| - same validated 100% coverage on embodiment/state/video/provenance fields |
| - custom normalization runs (other robots, other corpora, up to the full |
| 95,658-episode DROID) available on request |
|
|
| **Contact: energea@energeatech.com** |
|
|
| ## Honesty notes |
|
|
| - The source mirror's `task_category` and `date` columns are corrupt duplicates |
| of `building` and `collector_id` (verified) — excluded rather than shipped as |
| fake labels. |
| - `gripper_action_state` open/closed is a disclosed 0.5 threshold over the |
| continuous `gripper_position` (preserved verbatim). |
| - Instructions are never synthesized; unlabeled episodes stay unlabeled. |
|
|
| ## License |
|
|
| This packaged sample (the flat table, task construction, splits, HIN graph, |
| video index, and this documentation) is licensed **CC BY-NC 4.0** — |
| attribution required, **non-commercial use only**. Commercial use requires a |
| license — contact **energea@energeatech.com** (the full 1.7M-frame release is |
| sold under a separate commercial license). |
|
|
| **Scope note:** this NC restriction covers williamTLmiller's normalization and |
| packaging work. It does not and cannot restrict the underlying DROID |
| recordings themselves, which remain separately available under Apache |
| License 2.0 from their original source |
| ([`lerobot/droid_1.0.1`](https://huggingface.co/datasets/lerobot/droid_1.0.1)). |
| See `LICENSE` (CC BY-NC 4.0, this package) and |
| `LICENSE-APACHE-2.0-UNDERLYING-DATA.txt` (the source data's own license). |
|
|
| ## Attribution |
|
|
| Derived from [DROID](https://droid-dataset.github.io/) via the official |
| LeRobot-format mirror [`lerobot/droid_1.0.1`](https://huggingface.co/datasets/lerobot/droid_1.0.1) |
| (Apache-2.0). Please cite the DROID paper when using the underlying data. |
|
|
