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matrixnorm-roboticsgraph-droid-sample

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Robotics
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10K<n<100K
Tags:
robotics
manipulation
lerobot
droid
physical-intelligence
imitation-learning
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metadata 
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 
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). 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 via the official LeRobot-format mirror lerobot/droid_1.0.1 (Apache-2.0). Please cite the DROID paper when using the underlying data.